Recent advances on memetic algorithms and its applications in image processing 9789811513619, 9789811513626

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Studies in Computational Intelligence 873

D. Jude Hemanth B. Vinoth Kumar G. R. Karpagam Manavalan   Editors

Recent Advances on Memetic Algorithms and its Applications in Image Processing

Studies in Computational Intelligence Volume 873

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.

More information about this series at http://www.springer.com/series/7092

D. Jude Hemanth B. Vinoth Kumar G. R. Karpagam Manavalan •

•

Editors

Recent Advances on Memetic Algorithms and its Applications in Image Processing

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Editors D. Jude Hemanth Department of Electronics and Communication Engineering Karunya University Coimbatore, Tamil Nadu, India

B. Vinoth Kumar Department of Information Technology PSG College of Technology Coimbatore, Tamil Nadu, India

G. R. Karpagam Manavalan Department of Computer Science and Engineering PSG College of Technology Coimbatore, Tamil Nadu, India

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-981-15-1361-9 ISBN 978-981-15-1362-6 (eBook) https://doi.org/10.1007/978-981-15-1362-6 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Memetic algorithms (MAs) are a class of stochastic global search heuristics in which the problem-specific solvers are combined with meta-heuristics approaches. MAs might be implemented by incorporating heuristics, approximation algorithms, local search techniques, specialized recombination operators, truncated exact methods, etc., in meta-heuristics algorithms. The performance of MAs is strictly in accordance with the amount and quality of the problem knowledge they incorporate. Knowledge incorporation is meant to either to improve the solution quality, or to accelerate the convergence, or to reach solutions that would otherwise be unreachable by evolution or a local method alone. The necessity for the application of memetic algorithms in the field of image processing is increasing nowadays. This is one of the significant motivations behind the origin of this book. The book provides the original research findings in the field of memetic algorithms applied to the image processing applications. Different practical applications are covered in this book which will create an interest among the budding engineers in these areas. This book, indeed, is a wholesome product which will help the readers to grasp the extensive point of view and the essence of the recent advances in this field. A brief introduction about each chapter is as follows. Chapter “An Evolutionary Computing Approach to Solve Object Identification Problem for Fall Detection in Computer Vision-Based Video Surveillance Applications” covers the video surveillance application to detect the fall of elderly people. The main objective of this work is to provide a vision-based solution to detect the fall of elderly people without using any external devices such as sensors and accelerometers. Chapter “Texture-Dependent Optimal Fractional-Order Framework for Image Quality Enhancement Through Memetic Inclusions in Cuckoo Search and Sine-Cosine Algorithms” deals with memetic hybridization of cuckoo search optimizer and sine-cosine optimizer for image quality enhancement. This chapter introduces a novel framework for on-demand textural improvement of the image along with adaptive contrast enhancement.

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Chapter “Artificial Bee Colony: Theory, Literature Review, and Application in Image Segmentation” illustrates the theoretical background on artificial bee colony and image segmentation and also it reports the applications of artificial bee colony for medical image segmentation. Chapter “Applications of Memetic Algorithms in Image Processing Using Deep Learning” illustrates the applications of memetic algorithms over deep networks of the image processing-related applications. Also, it describes the image processing applications such as object detection, semantic segmentation, and damage detection in the vehicle industry. Chapter “Recent Applications of Swarm-Based Algorithms to Color Quantization” covers the swarm-based solutions for the color quantization problem which is one of the focal areas of research in image processing. A survey on the various hybrid biogeography-based optimization techniques for satellite image analysis is carried out in Chapter “Hybrid Biogeography-Based Optimization Techniques for Geo-Spatial Feature Extraction: A Brief Survey”. Chapter “An Efficient Copy-Move Forgery Detection Technique Using NatureInspired Optimization Algorithm” covers an efficient copy-move forgery detection method using nature-inspired optimization algorithm. It incorporated speeded-up robust features (SURF) framework-based detection scheme with particle swarm optimization (SURF-PSO) for forgery detection. Chapter “Design and Implementation of Hybrid Plate Tectonics NeighborhoodBased ADAM’s Optimization and Its Application on Crop Recommendation” reports the hybridization of plate tectonics neighborhood-based classifier and ADAM's algorithm to determine the most suitable crop to be grown in a particular region. This application is very significant in the context of crop recommendation in the agriculture field. Chapter “An Evolutionary Memetic Weighted Associative Classification Algorithm for Heart Disease Prediction” deals with the evolutionary memetic algorithm in the field of biomedical which helps the physicians to diagnose the heart disease in the patient at an early stage. We are grateful to the authors and reviewers for their excellent contributions for making this book possible. Our special thanks go to Prof. Dr. Janusz Kacprzyk (Series Editor of Studies in Computational Intelligence) for the opportunity to organize this edited volume. We are grateful to Springer, especially to Mr. Aninda Bose (Senior Editor) for the excellent collaboration. This edited book covers the fundamental concepts and application areas in detail which is one of the main advantages of this book. Being an interdisciplinary book, we hope it will be useful to a wide variety of readers and will provide useful information to professors, researchers, and students. Coimbatore, India October 2019

Dr. D. Jude Hemanth Dr. B. Vinoth Kumar Dr. G. R. Karpagam Manavalan

Contents

An Evolutionary Computing Approach to Solve Object Identification Problem for Fall Detection in Computer Vision-Based Video Surveillance Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katamneni Vinaya Sree and G. Jeyakumar 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Purpose and Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Current Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Theory and Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Investigation Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Heuristic Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Design and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Implementation of Feature Descriptors . . . . . . . . . . . . . . . . . . . 3.2 Implementation of DE Algorithm . . . . . . . . . . . . . . . . . . . . . . . 3.3 Implementation of EC Framework for Object Detection . . . . . . 3.4 Background Subtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 The Fall Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 BelgaLogos Image Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Fall Detection Without EC Framework . . . . . . . . . . . . . . . . . . . 4.4 Fall Detection with EC Framework . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Texture-Dependent Optimal Fractional-Order Framework for Image Quality Enhancement Through Memetic Inclusions in Cuckoo Search and Sine-Cosine Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Himanshu Singh, Anil Kumar, L. K. Balyan and H. N. Lee 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Proposed 2-D Fractional-Order Optimal Unsharp Masking Framework for Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Fitness Function Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Memetic Inclusions for Sine-Cosine Optimizer . . . . . . . . . . . . . . . . . 5 Memetic Inclusions in Cuckoo Search Optimizer . . . . . . . . . . . . . . . 6 Experimentation: Performance Evaluation and Comparison . . . . . . . . 6.1 Assessment Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Qualitative Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Quantitative Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Artificial Bee Colony: Theory, Literature Review, and Application in Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emrah Hancer 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Artificial Bee Colony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Applications of ABC for Image Segmentation . . . . . . . . . . . . . . . 3.1 Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Edge-Based Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Clustering-Based Segmentation . . . . . . . . . . . . . . . . . . . . . . 3.4 Learning-Based Segmentation . . . . . . . . . . . . . . . . . . . . . . . 4 Implementation of ABC to Brain Tumor Segmentation . . . . . . . . . 4.1 ABC-Based Segmentation Methodology . . . . . . . . . . . . . . . . 4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Applications of Memetic Algorithms in Image Processing Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Laby, M. Sudhakar, M. Janaki Meena and S. P. Syed Ibrahim 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Applications of Memetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Scheduling Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Machine Learning and Robotics . . . . . . . . . . . . . . . . . . . . . . . . 2.4 MAs in Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Building Blocks of CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Inputs and Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Structure of CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Convolution Layer (CONV) . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Pooling Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.5 Activation Function . . . . . . 3.6 Fully Connected Layer (FC) 4 Object Detection Using CNN . . . 5 Semantic Segmentation . . . . . . . . 5.1 E-Net Architecture . . . . . . . 6 Automatic Car Damage Detection 6.1 Transfer Learning . . . . . . . . 6.2 VGG Network . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . .

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Recent Applications of Swarm-Based Algorithms to Color Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . María-Luisa Pérez-Delgado 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Overview of Swarm-Based Methods . . . . . . . . . . . . . . . . . . . . 4 Swarm-Based Methods Applied to Color Quantization . . . . . . . 4.1 Artificial Ants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . 4.3 Artificial Bees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 The Shuffled Frog-Leaping Method . . . . . . . . . . . . . . . . . 4.6 A Brief Comparative Among Color Quantization Methods 4.7 Other Swarm-Based Methods . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hybrid Biogeography-Based Optimization Techniques for Geo-Spatial Feature Extraction: A Brief Survey . . . . . . . . . . Lavika Goel and Arshveer Kaur 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Categorization of Computational Intelligence Techniques . . . . . 3 Taxonomy of CI Techniques for Geo-Spatial Feature Extraction 4 Computational Intelligence Techniques for Feature Extraction . . 4.1 ACO2/BBO Intelligent Classifier . . . . . . . . . . . . . . . . . . . 4.2 Hybrid ACO2/PSO/BBO Optimization . . . . . . . . . . . . . . . 4.3 Hybrid FPAB/BBO Optimization . . . . . . . . . . . . . . . . . . . 4.4 BBO-GS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 ACO2/PSO/BBO-GS . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Critical Analysis of CI Techniques . . . . . . . . . . . . . . . . . . . . . 5.1 Classification Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Characteristic Comparison and Application Suitability of CI Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 An Efficient Copy-Move Forgery Detection Technique Using Nature-Inspired Optimization Algorithm . . . . . . Anmol Gupta and Ishan Chawla 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . 2.1 The SURF-Based Framework . . . . . . . . . . . . . . 2.2 Problems in the Parameter Value Selection . . . . . 3 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Elemental Detection . . . . . . . . . . . . . . . . . . 3.2 The Parameters Estimation . . . . . . . . . . . . . . . . 4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Design and Implementation of Hybrid Plate Tectonics Neighborhood-Based ADAM’s Optimization and Its Application on Crop Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lavika Goel, Navjot Bansal and Nithin Benny 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Plate Tectonics Neighborhood Optimizer with Adam’s Hybrid . . . 3.1 PBO Adam’s Hybrid Pseudo-Code . . . . . . . . . . . . . . . . . . . 3.2 Comparison of Hybrid of PBO SGD and Hybrid of PBO Adam’s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Plate Tectonics Neighborhood-Based Classifier . . . . . . . . . . . . . . . 4.1 Plate Accelerating Index (PAI(x, y)) . . . . . . . . . . . . . . . . . . . 4.2 Pseudo-Force Resultants . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Plate Tectonics Neighborhood-Based Classifier Pseudo-Code (Fig. 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Results of PBO Classifier on Soybean Dataset . . . . . . . . . . . 5.2 Performance Analysis on Independently Collected Dataset on Crop Production in States with Historical Dataset . . . . . . 5.3 Results on Crop Prediction Dataset . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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An Evolutionary Memetic Weighted Associative Classification Algorithm for Heart Disease Prediction . . . . . . . . . . . . . . . . . . S. P. Siddique Ibrahim and M. Sivabalakrishnan 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Association Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Associative Classification . . . . . . . . . . . . . . . . . . . . . . . 2.3 Weighted Associative Classification . . . . . . . . . . . . . . . . 2.4 Heart Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Memetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Evolutionary Memetic Associative Classification . . . . . . 4 Sample Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Editors

Dr. D. Jude Hemanth received his B.E. degree in ECE from Bharathiar University in 2002, M.E. degree in communication systems from Anna University in 2006, and Ph.D. from Karunya University in 2013. His research areas include computational intelligence and image processing. He has authored more than 100 research papers in reputed international journals, as well as one book (with VDM Verlag, Germany) and numerous book chapters with major publishers such as Springer and Inderscience. He has served as an associate editor of IEEE Access Journal. He is also a guest editor for many journals with Springer, Inderscience, IOS Press, etc. A member of the IEEE Technical Committee on Neural Networks (IEEE Computational Intelligence Society) and IEEE Technical Committee on Soft Computing (IEEE Systems, Man and Cybernetics Society), he has completed one funded research project for the CSIR, Government of India. Currently, he is working as an Associate Professor at the Department of ECE, Karunya University, Coimbatore, India. Dr. B. Vinoth Kumar received the B.E. degree in Electronics and Communication Engineering from the Periyar University in 2003, and the ME and Ph.D. degrees in Computer Science and Engineering from the Anna University in 2009 and 2016, respectively. He is an Associate Professor with 15 years of experience at PSG College of Technology. His current research interests include Computational Intelligence, Memetic Algorithms, and Image Processing. He has established an Artificial Intelligence Research (AIR) Laboratory along with Dr. G R Karpagam at PSG College of Technology. He is a Life Member of the Institution of EngineersIndia (IEI), International Association of Engineers (IAENG) and Indian Society of Systems for Science and Engineering (ISSE). He has published papers in peer reviewed National/International Journals and Conferences and a Reviewer of International Journals.

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About the Editors

Dr. G. R. Karpagam Manavalan is Professor with 20 years of experience in Computer Science and Engineering at PSG College of Technology. Holding a B.E., M.E., and Ph.D. in Computer Science and Engineering, her areas of specialization include database management systems, data structures and algorithms, serviceoriented architectures, cloud computing, model-driven architectures, and security. She is a member of many academic and professional associations, e.g., the IEEE, ACM, ISTE, IE Institution of Engineers, and ACCS.

An Evolutionary Computing Approach to Solve Object Identification Problem for Fall Detection in Computer Vision-Based Video Surveillance Applications Katamneni Vinaya Sree and G. Jeyakumar

Abstract This chapter proposes to present the design and development of an evolutionary computation (EC) based system which solves two interesting real-world problems. In the first phase of the system, the object detection problem of image processing is considered. An EC framework is designed to solve it, by formulating it as an optimization problem, considering the minimal required features to detect an object in a given image. The differential evolution (DE) algorithm is used in the framework. The objectives considered are to maximize the accuracy of detection and minimize the number of features required. Belga LOGOS dataset is considered, and 10 different feature descriptors are individually applied for object detection. Later applied the combination of features using the proposed EC framework and compared the results. Individual descriptors gave an average accuracy of 72.35% whereas the proposed method gave an average accuracy of 81.15% which shows the efficiency of the proposed framework. In the second phase, a real-world problem of fall detection of elderly people is considered for surveillance applications. A vision-based solution is proposed instead of using any external devices such as sensors and accelerometers to detect the fall. The advantage of this method is that it doesn’t need the subject to carry any devices and is cost-effective as it is based on only the surveillance videos. To detect the fall, the person needs to detected and tracked during their activities. Initially, people are tracked using core computer vision technique later applied the EC framework developed in Phase I to detect the people and the results are compared. Core computer vision techniques gave an accuracy of 97.16% whereas the proposed optimization framework gave an accuracy of 98.65%. This proved the efficiency of the proposed approach. K. V. Sree
G. Jeyakumar (&) Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] K. V. Sree e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_1

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1 Introduction In the modern era, technology is used to the core of its innovation in which each and every part of the science can be combined with other approaches so as to make them best utilized with maximum efficiency. Deriving meaning from what’s seen is vision and is a complex process. Image processing and computer vision have been trying to automate almost all the tasks that a human visual system does, aiming to gain next level of understanding from images and videos. A lot of real-world problems such as human–robot interaction and video surveillance to remote sensing are solved by successfully applying computer vision algorithms. Recently, the integration of evolutionary computing (EC) methods with computer vision algorithms attracts the research community. They are proven to be more efficient than the classical optimization algorithms. They have been proving their ability to cope up with any of the fundamental problems of computer vision.

1.1

Background

Increase in population consisting of elder people day by day demands an improvement in healthcare systems. A lot of them are staying alone in the house without anyone to take care of them. In such conditions, the major care that needs to be considered is the injuries caused due to fall. From [1], it is evident that 59% of the elderly people are seeking care for injuries caused due to fall and there would be a 70% increase in the amount spent for fall-related injuries. The studies in this domain are categorized as fall prevention, fall detection, and injury reduction. In fact, prevention of fall is a tough task as it needs faster response and reaction. However, solutions can be generated to protect the person falling at the moment of fall [2]. Hence, methodologies proposing solutions for accurate and early detection of fall increase the chances for reducing the loss after the fall.

1.2

Purpose and Goal

After studying the previous works related to fall detection, it is understood that there are several applications to detect fall but the major drawback of these applications is the subject that is required to carry a device with him always so that whenever he/she falls it gets detected. But it is not possible for a patient/an elderly person to carry a device with him/her. So now the question is how to avoid such wearable devices in detecting fall. Hence, the need for approaches using vision-based algorithms with the input from surveillance video to continuously monitor the subject of interest arises.

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3

Current Solutions

The currently available solutions to detect the fall are hardware-based and need wearable devices to be carried. Apart from these, there are vision-based approaches proposed but they use videos along with accelerometers. Some of the existing works presented in [3–5] use different types of sensing devices and detect the abnormal motions which relate to falls in daily life. But the disadvantage of using these sensors is that they need a lot of data to determine the normal behavior. So to optimize this detection process, an EC-based fall detection system with computer vision technique is proposed in this article.

2 Theory and Techniques 2.1

Investigation Phase

In this phase, studies are conducted on detecting fall of human from videos. The studies revealed that first the human is to be detected and tracked from the video to detect his/her fall. The challenges in this vital task are choosing the correct approach and selecting the required features, for human detection. After detecting the human, the next challenge is in tracking him. This should be done with utmost care. After detecting and tracking the person, the next major challenge is in identifying the features to be considered for detecting the fall. The system proposed resolves all these challenges and includes heuristic evaluations on each of the challenges.

2.2

Heuristic Evaluation

In this phase, the available feature descriptors, approaches to detect the foreground, and features that need to be considered for fall detection are investigated.

2.2.1

Available Feature Descriptors

There are a number of feature descriptors that are used to detect query objects in a scene image, viz. template matching (Coeff), template matching (Corr), template matching (SQDiff), canny edge, sliding window, Harris corner, FLANN, ORB, feature matching homography, and SIFT ratio. Each of this descriptor plays its own role in object detection. However, when they are applied individually in an object detection process, they fail in tackling the challenges occur with respect to the changes in the target object in the given image scene. In the cases of using the

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template matching, the query image is reframed as small portions and then these portions are compared on the image scene. This feature descriptor is good in detecting even small query images. But it fails in detecting the query images which are in the transformed (rotated, scaled, mirrored, or occluded) form in the image scene. A computationally expensive technique is sliding window technique, and it takes the image scene in an evenly spread grid and scans it to check the presence of the query image. The probability of detecting an object is less in this technique. The SIFT features have the limitation of mathematical complication and heavy computation. However, they are good for object detection even with the image scenes having cluttered background. Since they use the histogram of gradients of each pixel, they fail for images from low power devices. The authors in [6] propose an approach for palm print feature extraction using Harris corner detector. The Harris corners detected are used to extract the corner information. This information is matched with the Hamming distance similarity with the other feature vectors. The less distance indicates the presence of the query image in the image scene at that position. In [7], the feature points-based comparison is done between the query image and the image scene. On detecting the query image in the image scene, the detection of its exact position is a difficult task. Reason for this issue is that this approach considers only the regions in the image which has drastic intensity changes. This issue is even addressed in [7], by reducing the number of features to be considered during detection. The authors in [8] also propose a solution to solve this issue by using the sliding window approach. It follows a strategy to optimize the window size also. Though the approaches in [6, 7] lead to good accurate solution, their combination also tried in [6]. This experiment found that this combined approach gives drastic improvement in the accuracy of the solution. It highlights the significance of combining the feature descriptor (fds). However, testing this on increased number of fds needs approaches for selecting suitable fds, which is the focus of the Phase I work of the system proposed. An optimized template matching method [by particle swarm optimization (PSO) algorithm] is used in [9] with other methodologies such as least squares window matching and template matching with fast normalized cross-correlation. It proved its superiority for quality solution, faster convergence, and efficient computation. However, this approach raised difficulties in detecting moving and changing objects, changing illumination, and changing viewpoints. The approach presented in [10] uses Hopfield neural network integrated with genetic algorithm (GA) for object detection. For the given shape, the suitable features are extracted by a polynomial approximation technique for the required dimension. This approach is also good at detecting the overlapped objects. It is also found that this approach fails at the cases when the problems to be solved with larger population size. Similar to this an approach using GA along with Fourier descriptor is presented in [11]. This has the support of detecting objects with different viewpoints. This also works for transformed images and occluded images. In spite of all the above advantages, this approach is also facing the challenge of selecting appropriate combination of the fds.

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The approaches for object detection, in the literature, which use the concept of combinations of fds suffer with the challenge of identifying, listing, and selecting appropriate fds during their process. Considering this challenge, this work in Phase I proposes an EC approach which uses DE algorithm to generate appropriate combination of fds to identify the objects in a given scene image.

2.2.2

Detecting Foreground Object

The challenge in tracking the human in the video is detecting the foreground. The available techniques for this are background subtraction, temporal average filtering, and conventional approaches. The conventional approaches use frame difference, mean filter Gaussian average, and background mixture models. All these are highly variable depending on lighting, interiors, exteriors, quality, and noise of the videos. The proposed system employs Gaussian mixture model which is robust to these variations.

2.2.3

Features to Detect Fall

To detect fall, lot of features can be considered. The moving person is continuously tracked, and the detection is represented using a bounding box around the moving object. References [12–14] explained how these moving objects can be detected in the video. This helps to formulate the procedure to detect and extract the required features from the bounding box. The features considered are aspect ratio, horizontal and vertical gradients, fall angle, acceleration, height, and falling speed [15]. The usage of the measures for fall detection [15] is explained below. Aspect Ratio This is a good measure to detect the change of posture of a person, particularly from the standing position to any abnormal position. Horizontal and Vertical Gradients They represent the X and Y values of the person detected. These values get changed with the posture change of the person. Fall Angle This is measured as the angle between the centroid of the falling object and its horizontal ratio. When this value goes less than 45, it is the indication that the person falls. But for correct detection, the fall angle to be used along with other measurements. Change in Height This measurement calculates the difference between the mean height of the person body and the height at the detection process.

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3 Design and Implementation 3.1

Implementation of Feature Descriptors

Available feature descriptors described in previous sections are implemented using OpenCV library in python programming language. Initially individual feature descriptors are used to detect the object and observed the accuracy at which the objects are detected.

3.2

Implementation of DE Algorithm

The differential evolution (DE) algorithm is proposed by Storn and Price [16–18] in 1995. It solves the optimization problems by randomly as well as systematically searching the global optimal solution from an initial set of random possible solutions. This set is called as population. Each solution in this population is a Ddimensional vector. Here, D represents the number of variables in the problem at hand. To cover all possible solutions, in the initial stage itself, the population is generated uniformly randomly. An iterative process which generates new and diverse candidates using the existing candidates is initiated. This process is repeated until a required stopping criterion is met. The operators involved in this iterative process are mutation, crossover, and selection. The simple algorithmic structure of DE attracted many researchers to understand its behavior [19–23], improve its performance by distribution and/or parallelization [24, 25], and use it for different real-world problems [26–33]. Given a set of d features, the process of selecting a subset of m features leading to greater detection accuracy can be modeled as an optimization problem. This process is to search and find an optimal subset from all possible subsets of the features. This can be modeled as optimal, randomized, or a heuristic search process. An exhaustive search technique can be used, but the possible subsets increase with the number of features. There are strategies to detect local best subset, viz. sequential forward and sequential backward selection. But they are unfit to detect the global best subsets. There are randomized strategies for this purpose. They use probabilistic sampling step. A weight is assigned for each feature randomly and used for detecting the solution subset. This approach is biased to the random weights. It is clear that each of the approach in the list for the feature selection process has a drawback in reaching the global best subset of features. The proposed system, which uses DE for feature selection, overcomes these challenges and able to find the global best subset by its randomized, sequential, and systematic search. This system does not need any prior knowledge about the problem being solved.

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Implementation of EC Framework for Object Detection

The proposed approach uses all the feature matching techniques mentioned in Sect. 2.2.1 to detect scale variant objects. The study is carried out with four different images for object detection. Initially, the experiments are done with individual fds. Then the DE-based approach is employed to detect the subset of fds from the whole set. The fds in the subset are combined, and they are used together for object detection. The DE is able to select optimum subset which leads the object detection process more accurate. During the selection process, the algorithm assigns a weighting factor to the fds which are used as relative importance of the fds. These values are then used to find the optimal subset of fds. The control parameters of DE and the values set for them are as follows (as mentioned in [34]): population size (NP) = 50, mutation step size (F) = 0.3, crossover rate (R) = 0.7, mutation type (Mt) = rand/1, and the crossover type (Cr) = binomial.

3.4

Background Subtraction

To extend the object detection module of the system to fall detection of human, the surveillance videos are taken as the input. The video monitoring a human is continuously streamed as input to this fall detection module. A background subtraction process is initiated to partition the pixels as foreground and background from the video. This process employs the MOG2 subtractor of OpenCv with the Gaussian mixture model. This helped to differentiate the background and foreground image even with less variances.

3.5

The Fall Detection System

The flow diagram of fall detection module of the system is shown in Fig. 1 (referring to [15]). As it is shown in Fig. 1, the streamed video is divided into frames first. Then the background subtraction process is initiated. Next the preprocessing steps are carried out on the frames. From the preprocessed frames, the features required to detect the fall are extracted. Finally, with the extracted features a finite state machine-based procedure is carried out to check the person is falling or not. The fall detection module includes a contour finding process with the background subtraction to detect and then to track the human objects in the frame. As it is the vital and critical task in this system, at most care is taken while implementing the proposed system for human detection and tracking. Initially, the background subtraction process is used to get the moving objects in the video. Followed by that, the foreground segmentation is carried out. The noise elimination is done using the morphological operations. This module integrates various vision techniques on

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Fig. 1 Fall detection system (figure courtesy [15])

video and identifies the moving objects. Then the moving objects (human) are continuously tracked for the fall detection. At the initial stage of feature extraction phase, this module considers the contours and bounding box parameters. Later the EC framework is initiated for feature extraction. The extracted features are used to construct a rule-based finite state machine (fsm). This fsm is then used to capture and analyze the bounding box values continuously. The state transition system of fall detection is modeled by a three-state fsm with 8 rues. The states used in the fsm are Fall_detection, Posture _changed, and Fall_confirmation [15]. A detailed view of this fsm is depicted in Fig. 2 (referring to [15]). The state transitions in the fsm are taking place with respect to the 8 rules framed (as given in [15]). The rules are Rule 1—True, if the aspect ratio is between 0 and 1, Rule 2—True, if the horizontal gradient is greater than the vertical gradient, Rule 3 —True, if the fall angle less than 45°, Rule 4—True, if the acceleration is lower than 130 cm2, Rule 5—True, if the height is less than 75 cm, Rule 6—True, if the ratio of the height of the person to his mean height is less than 0.45, Rule 7—True, if the time difference between standing posture and when the detection of lay down is found is less than a threshold, and Rule 8—True, if there is no or little movement after the fall. When a person falls, the height and weight of the bounding box change rapidly and hence the aspect ratio of the human body. So, the aspect ratio is used as the measure for Rule 1. In standing or walking position of human, their horizontal gradient is less than the vertical gradient. This comparison is used for checking the Rule 2. The fall angle measured from the centroid coordinates, i.e., the mass

Fig. 2 fsm for fall detection system (figure courtesy [15])

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coordinate of the object is used for Rule 3. The acceleration factor is used for Rule 4. The height of a person should be decreasing during a fall, and this observation is used for framing another rule (Rule 5). In the existing works [11], the standing and lying positions of the human are used to check the posture of the person. The Rule 6 compares the height of a person at time t with his mean height. The falling speed is the measurement used in Rule 7. It detects the speed at which the fall of a person occurs. The Rule 8 is to generate the alarm at final, and it checks the movement condition of the person after fall. The transitions of states during the fall detection process are taking place with the True/False conditions of the rules described.

4 Experimental Results 4.1

BelgaLogos Image Dataset

The BelgaLogos dataset is used to evaluate the performance of the proposed fall detection module. This dataset contains 10,000 images from the Belga press agency, annotated with the location and name of brand logos appearing in the images. In total, annotations for 26 separate logos are provided in the dataset, covering a variety of businesses. There are ten thousand images in the dataset which are manually annotated. They provide a global and a local ground truth. In global ground truth, each image is labeled for each of the logos with 1 if logo is present, 0 when there is no logo in the scene image. It also provides the localization for each of the logos in the image in the form of four values such as top left, top right, bottom left, and bottom right which in turn forms a bounding box around the logo in the image. In local ground truth, there are bounding boxes for each of the 37 logos and are annotated OK if any of the logo’s presence is detected in the image, otherwise it is annotated as JUNK. Three distinct pools of queries can be used for evaluation: Qset1, Qset2, and Qset3. Query set Qset1 has 55 internal queries and each image has a name and the bounding box coordinate values. Query set Qset1 contains the images of the logos, downloaded as thumbnails each having the name of the logo as its name. All these are resized images of the images that are downloaded from Google. Query set Qset3 is chosen for testing the proposed technique as it has a similarity in output such as giving out a bounding box around the object that is detected in the scene image. The evaluation metric that is used is average accuracy of detecting the objects in the scene images with different number of features that are chosen by DE algorithm. The average detection accuracy over all the images is found and compared it with different runs of DE algorithm.

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Object Detection

The EC framework with DE is implemented to solve the object identification problem, first. Table 1 shows the detection accuracies for 6 images from the dataset taken for each feature descriptor individually. Different instances of runs are carried out for in-depth analysis. It is observed that each instance rendered different detection accuracies and with varying number of fds. This is due to the random nature of DE algorithm, which brings the changes in the number and the types of fds selected for each run (Table 2). From the results, it is observed that individual descriptors gave the average accuracy of 74.13, 47.47, 89.95, 85.21, 55.27, and 84.31 for the Images 1–6, respectively. The proposed method gave the average accuracy of 85.89, 65.16, 93.16, 87.98, 70.03, and 84.65 for the Images 1–6, respectively. The proposed approach has outperformed in all the images. The overall average accuracy in the case of using individual descriptor is 72.35 and in the case of using proposed approach is 81.15. This shows the efficiency of the proposed framework. The output screenshots of the resulting object detection are shown in Fig. 3. After observing the results for a sample of 25 images from the query Qset3 of the dataset, it is observed that some of the feature descriptors (individually) have not detected the objects at all in the image scene. One of the reasons for this is that the object in the query is occluded or only a part of it is visible in the image scene. The descriptors such as template matching are capable of matching the object when they are completely present in the image scene. The other reason identified is the

Table 1 Individual fds and the detection accuracies Name of the fd

Detection accuracy for Image 1 Image 2 Image 3

Image 4

Image 5

Image 6

Template matching (Coeff) Template matching (Corr) Template matching (SQDiff) Canny edge Sliding window Harris corner FLANN ORB Feature matching homography SIFT ratio Average accuracy Maximum

83.45 68.45 79.43 42.45 83.25 68.57 73.27 65.73 89.02

0.00 0.00 0.00 53.55 45.35 75.34 68.45 79.33 66.97

93.39 74.46 89.50 97.21 96.23 91.72 83.78 75.32 93.55

87.39 64.29 89.77 87.57 83.56 90.93 85.54 87.37 85.56

0.00 0.00 0.00 63.55 73.42 84.34 83.45 76.33 91.97

92.39 81.20 91.73 85.96 72.76 87.12 79.23 91.22 71.43

87.77 74.13 89.02

65.71 47.47 79.33

99.34 89.95 99.34

90.15 85.21 90.93

79.71 55.27 91.97

90.15 84.31 92.39

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Table 2 Detection accuracies with combined fds selected by DE Images

No. of fds

Detection accuracy

No. of fds

Image 1

7

87.45

5

Detection accuracy

No. of fds

85.21

4

Detection accuracy

No. of fds

79.34

3

Detection accuracy

Avg.

Max

91.58

85.89

91.58

Image 2

57.34

62.35

59.45

81.53

65.16

81.53

Image 3

91.32

89.25

93.53

98.54

93.16

98.54

Image 4

86.92

83.86

91.23

89.92

87.98

91.23

Image 5

59.01

57.29

76.28

87.54

70.03

87.54

Image 6

89.67

83.51

78.39

87.05

84.65

89.67

Fig. 3 Object detection in image from dataset

presence of query image at multiple places in the scene image. This makes some of the descriptors to take the object that it first detects and stops. But in some applications, it is required to detect multiple occurrences of the same object in the image scene.

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Fall Detection Without EC Framework

The three datasets on which the proposed fall detection module is experimented are URD, FDD, and Multicam [8, 35, 36]. The URD dataset (70 samples) includes 30 samples for fall events and 40 samples for regular events. The FDD with 24 samples has 22 with fall and 2 with non-fall events. It also includes different environments such as office, lecture hall, coffee room, and home. Table 3 shows the results obtained by the proposed fall detection module (referring to [15]). Here, TP represents the number of fall events identified as fall events and TN represents the number of non-fall events identified as non-fall events. FP represents number of non-fall events identified as fall, and FN represents number of fall events identified as non-fall.

4.3.1

Performance Metrics for Fall Detection

The performance metrics used for evaluating the fall detection process are Accuracy, Sensitivity, and Specificity. They are measured by Eqs. (1), (2), and (3), respectively Accuracy ¼ TN þ TP=ðTP þ TN þ FP þ FNÞ

ð1Þ

Sensitivity ¼ TP=ðTP þ FNÞ

ð2Þ

Specificity ¼ TN=ðTN þ FPÞ

ð3Þ

where TP is true positives, FP is false positives, TN is true negatives, and FN is false negatives. The values measured for the performance metrics are presented in Table 4 (referring to [15]). These results are obtained before integrating EC framework with the fall detection module. The average fall detection accuracy, sensitivity, and specificity are 97.16%, 98.13%, and 96.45%, respectively. Sample frames identified for possible fall

Table 3 Recognition results without EC framework Dataset

No. of events

TP

FP

TN

FN

URD Multicam FDD Office FDD Lecture room FDD Coffee room FDD Home

70 24 30 30 30 30

30 21 15 14 15 15

2 0 2 0 0 0

38 2 13 15 15 15

0 1 0 1 0 0

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Table 4 Accuracy, sensitivity, and specificity of fall detection without EC framework Dataset

No. of events

Accuracy (%)

Sensitivity (%)

Specificity (%)

URD Multicam FDD Office FDD Lecture room FDD Coffee room FDD Home Average

70 24 30 30 30 30

97.14 95.83 93.33 96.67 100 100 97.16

100 95.45 100 93.33 100 100 98.13

95 100 86.67 100 100 100 96.95

Fig. 4 Frames used at fall detection (figure courtesy [15])

detection cases are shown in Fig. 4, for reference. Few images with actual fall and non-fall categories detected by this fall detection module are shown in Figs. 5 and 6. It is found in the comparative analysis that this fall detection module outperforms then existing approaches presented in [37, 38], with the detection accuracy of 97.16% and with different environments and camera positions.

4.4

Fall Detection with EC Framework

After employing the EC framework, the results obtained by the proposed fall detection module are presented in Table 5. The corresponding performance metric measures are presented in Table 6. The results show that the proposed approach can detect a fall with an average accuracy of 98.65% and sensitivity of 97.78% and specificity of 96.94%. It is seen from the results that the fall detection accuracy is increased by integrating the EC framework with the fall detection module.

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Fig. 5 Sample images for fall events

Fig. 6 Sample images for non-fall events

K. V. Sree and G. Jeyakumar

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Table 5 Fall detection results with EC framework Dataset

No. of events

TP

FP

TN

FN

URD Multicam FDD Office FDD Lecture room FDD Coffee room FDD Home

70 24 30 30 30 30

30 22 15 14 14 15

2 0 2 0 0 0

38 2 13 15 15 15

0 0 0 1 1 0

Table 6 Performance of the fall detection module with EC framework Dataset

No. of events

Accuracy

Sensitivity

Specificity

URD Multicam FDD Office FDD Lecture room FDD Coffee room FDD Home Average

70 24 30 30 30 30

98.57 100 96.67 96.67 100 100 98.65

100 100 100 93.33 93.33 100 97.78

95 100 86.66 100 100 100 96.94

5 Conclusions This article presented the results of solving two real-world problems in computer vision, with the integration EC-based framework. In Phase I, the objection detection problem is solved by combining optimal set of feature descriptors from the whole with increased accuracy. In Phase II, the problem of fall detection of elderly people is taken for study. Treating this as a surveillance application, a vision-based solution is proposed without using any external devices such as sensors and accelerometers to detect the fall. To detect the fall, the person is to be detected in the video and to be tracked during their activities. Initially, people are tracked using core computer vision technique later applied the EC framework developed in Phase I, and the results are compared. Core computer vision techniques gave the accuracy of 97.16% whereas the proposed approach gave the accuracy of 98.16% and proved its efficiency. The advantage of the proposed method is its high accuracy in detecting the fall at any environmental change or camera positions. This approach doesn’t need the person to carry any wearable device; it can solely be done based on a surveillance video. Moreover, this is a cost-effective system as it doesn’t need any special devices to achieve the objective. Future enhancement of this system is to employ deep learning technique to speed up the computation. An addition of an alert system specific to person and the venue for necessary immediate action would be a better enhancement of this system. Once

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the fall of the person is detected and confirmed, establishing an email to the concerned person regarding the fall is mandatory and will be implemented in future work.

References 1. Büchele, G., Becker, C., Cameron, I.D., Köning, H.-H., Robinovitch, S., Rapp, K.: Epidemiology of falls in residential aged care: analysis of more than 75,000 falls from residents of bavarian nursing homes. Jamda 15(8), 559–563 (2014) 2. Hövding. Hövd den nyacyklehjälmen. http://www.hovding.se/ (2015) 3. Broadley, R.W., Klenk, J., Thies, S.B., Kenney, L.P.J., Granat, M.H.: Methods for the real-world evaluation of fall detection technology: a scoping review. In: Sensors (Basel), vol. 18, no. 7, pp. 2060 (2018) 4. Tao, X., Zhou, Y., Zhu, J.: New advances and challenges of fall detection systems: a survey. Appl. Sci. (2018). https://doi.org/10.3390/app8030418 5. Birku, Y., Agrawal, H.: Survey on fall detection systems. Int. J. Pure Appl. Math. 118(18), 2537–2543 (2018) 6. Malik, J., Kurukshetra, G., Sainarayanan, G: Harris operator corner detection using sliding window method. Int. J. Comput. Appl. 22(1) (2011) 7. Lee, J., Bang J., Yang, S.I.: Object detection with sliding window in images including multiple similar objects. In: Proceeding of International Conference on Information and Communication Technology Convergence (ICTC), pp. 803–806 (2017) 8. Charfi, I., Mitéran, J., Dubois, J., Atriand, M., Tourki, R.: Optimized spatio-temporal descriptors for real-time fall detection: comparison of SVM and Adaboost based classification. J. Electr. Imaging (JEI) 22(4), 17 (2013) 9. Sharma, A., Singh, N.: Object detection in image using particle swarm optimization. Int. J. Eng. Technol. 2(6) (2010) 10. Huang, J.-S., Liu, H.-C.: Object recognition using genetic algorithms with a hopfield’s neural model. Expert Syst. Appl. 13(3), 191–199 (1997) 11. ul Hassan, M., Sarfraz, M., Osman, A., Alruwaili, M.: Object recognition using particle swarm optimization and genetic algorithm. Int. J. Comput. Sci. Iss. 10(5) (2013) 12. Sreelakshmi, S., Vijai, A., Senthil Kumar, T.: Detection and segmentation of cluttered objects from texture cluttered scene. In: Proceedings of the International Conference on Soft Computing Systems, vol. 398. Springer, Berlin, pp. 249–257 (2016) 13. Parameswaran, L.: A hybrid method for object identification and event detection in video. In: National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE Explore, Jodhpur, India, pp. 1–4 (2013) 14. Houacine, A., Zerrouki, N.: Combined curvelets and hidden Markov models for human fall detection. In: Multimedia Tools and Applications, pp. 1–20 (2017) 15. Vinaya Sree, K., Jeyakumar, G: A computer vision based fall detection technique for home surveillance. In: Proceedings ISMC (2018) 16. Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report 95–012, ICSI (1995) 17. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11, 341–359 (1997) 18. Price, V.: An introduction to differential evolution. In: New Ideas in Optimization, pp. 79–108 (1997) 19. Akhila, M.S., Vidhya, C.R., Jeyakumar, G.: Population diversity measurement methods to analyze the behavior of differential evolution algorithm. Int. J. Control Theor. Appl. 8(5), 1709–1717 (2016)

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20. Thangavelu, S., Jeyakumar, G., Shunmuga Velyautham, C.: Population variance based empirical analysis of the behavior of differential evolution variants. Appl. Math. Sci. 9(66), 3249–3263 (2015) 21. Gokul, K., Pooja, R., Gowtham, K., Jeyakumar, G.: A self-switching base vector selection mechanism for differential mutation of differential evolution algorithm. In: Proceedings of ICCSP-2017—International Conference on Communication and Signal Proceedings (2017) 22. Devika, K., Jeyakumar, G: Solving multi-objective optimization problems using differential evolution algorithm with different population initialization techniques. In: Proceedings of 2018 International Conference on Advances in Computing, Communications and Informatics, pp. 1–57 (2018) 23. Gokul, K., Pooja, R., Jeyakumar, G.: Empirical evidences to validate the performance of self-switching base vector based mutation of differential evolution algorithm. In: Proceedings of 7th International Conference on Advances in Computing, Communications and Informatics, pp. 2213–2218 (2018) 24. Jeyakumar, G., ShunmugaVelayutham, C.: Distributed mixed variant differential evolution algorithms for unconstrained global optimization. Memet. Comput. 5(4), 275–293 (2013) 25. Jeyakumar, G., Shunmuga Velayutham, C.: Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization. Soft Comput. 18(10), 1949–1965 (2014) 26. Haritha, T., Jeyakumar, G.: Image fusion using evolutionary algorithms: a survey. In: Proceedings of ICACCS 2017 (2017) 27. Abraham, K.T., Ashwin, M., Sundar, D., Ashoor, T., Jeyakumar, G: An evolutionary computing approach for solving key frame extraction problem in video analytics. In: Proceedings of ICCSP-2017—International Conference on Communication and Signal Processing (2017) 28. Rubini, N., Prashanthi, C.V., Subanidha, S., Sai Vamsi, T.N., Jeyakumar, G: An optimization framework for solving RFID reader placement problem using greedy approach. In: Proceedings of ICACCI-2017—6th International Conference on Advances in Computing, Communications and Informatics (2017) 29. Abraham, K.T., Ashwin, M., Sundar, D., Ashoor, T., Jeyakumar, G.: Empirical comparison of different key frame extraction approaches with differential evolution based algorithms. In: Proceedings of ISTA-2017—3rd International Symposium on Intelligent System Technologies and Applications (2017) 30. Jeyakumar, G., Nagarajan, R.: Algorithmic approaches for solving RFID reader positioning problem with simulated and real-time experimental setups. In: Proceedings of 7th International Conference on Advances in Computing, Communications and Informatics, pp. 1383–1387 (2018) 31. Rubini, N., Prashanthi, C., Subanidha, S., Jeyakumar, G.: An optimization framework for solving RFID reader placement problem using differential evolution algorithm. In: Proceedings of International Conference on Communication and Signal Proceedings (2017) 32. Abraham, K.T., Ashwin, M., Sundar, D., Ashoor, T., Jeyakumar, G.: Empirical comparison of different key frame extraction approaches with differential evolution based algorithms. In: Proceedings of International Symposium on Intelligent System Technologies and Applications (2017) 33. Jeyakumar, G., Sreenath, K.: Personalized courseware construction using association rules with differential evolution algorithm. In: Proceeding of International Conference on Advances in Computer Science, Engineering and Technology (2018) 34. Vinaya Sree, K., Jeyakumar, G.: An evolutionary computing approach to solve object identification problem in image processing applications. In: Proceedings of 1st International Conference on Intelligent Computing (ICIC 2018) (2018) 35. Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Multiple cameras fall dataset. Technical report 1350, DIRO—Université de Montréal (2010)

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36. Kwolek, B., Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Progr. Biomed. 117(3), 489–501 (2014). ISSN 0169-2607 37. Nasution, A.H., Emmanuel, S.: Intelligent video surveillance for monitoring elderly in home environments. In: Proceedings of the IEEE 9th International Workshop on Multimedia Signal Processing (MMSP’07), pp. 203–206. Crete, Greece (2007) 38. Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall detection from human shape and motion history using video surveillance. In: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), pp. 875– 880 (2007)

Texture-Dependent Optimal Fractional-Order Framework for Image Quality Enhancement Through Memetic Inclusions in Cuckoo Search and Sine-Cosine Algorithms Himanshu Singh, Anil Kumar, L. K. Balyan and H. N. Lee

Abstract In this contemporary era, technological dependencies on digital images are indispensable. Quality enhancement is an obligatory part of image pre-processing, so that desired information can be harvested efficiently. The varying texture in any image contributes to the information about the structural arrangement of surface content of the captured scene. Fractional-order calculus (FOC) and its related optimally ordered adaptive filtering are quite appreciable. Especially for texture preserved image quality enhancement, analytical strength of FOC is latently too valuable to be casually dismissed. No any closed-form free-lunch theory survives for evaluating the required fractional-order for overall quality enhancement because non-linear features of images from diverse domains require highly adaptive on-demand processing. Hence, texture preserved image quality enhancement can be considered as an NP-hard problem, where there isn’t an exact solution that runs in polynomial time. Thus, by the virtue of evolutionary algorithms along with their associated swarm intelligence, a near-exact solution can be attained. Memetic hybridization of cuckoo search optimizer (CSO) and sine-cosine optimizer (SCO) for this purpose is the core contribution in this chapter. In this chapter, to support the theoretical discussion in the context of the fundamentals behind CSO and SCO, their mathematical beauty of convergence is also highlighted which itself has resulted from the balance exploration and exploitation behavior. A novel texture-dependent objective function is also proposed in this chapter for imparting the patch-wise overall texture preserved image quality enhancement. Finally, the comparative analysis of results illustrates the superior capability of the proposed approach.

H. Singh (&)
A. Kumar
L. K. Balyan Indian Institute of Information Technology Design and Manufacturing, Jabalpur, India e-mail: [email protected] H. N. Lee Gwangju Institute of Science and Technology, Gwangju, South Korea © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_2

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Keywords Texture Fractional-order calculus Adaptive filtering Memetic intelligence Cuckoo search algorithm Sine-cosine algorithm Image enhancement










1 Introduction Digital imagery has completely revolutionized the way of analysis and investigation in this technological era. Big data analysis and the advances in the computational powers have increased the dependency over the images for information processing up to a great extent. Whenever it is desired to harvest the information through an image, it is required to pre-process the acquired image. Due to poor-illumination conditions and inadequate environmental behavior, image acquisition suffers a lot especially in the case of remotely sensed images. Also, due to long capturing distances and unbalanced natural illumination, many of these kinds of images are acquired as dark images. Hence, quality enhancement is highly desirable in such cases. In the beginning of digital image processing, researchers had suggested various manner of histogram-based approaches like histogram equalization (HE), histogram matching, and histogram sub-equalization. Afterward, various other variants of the sub-histograms-based processing were also proposed. Various experiments in the state-of-the-art literature indicated toward the gaps and limitations, as they seem unable to preserve the local spatial features of the images. Authors in [1, 2] have used fuzzy inspired smoothening for histogram, followed by peak-based histogram sub-division, also known as brightness preserving dynamic fuzzy HE (BPDFHE), but excellence of this approach is limited only for the images having significant peaks in the histograms. Along with it, median-mean dependent sub-image-clipped HE (MMSICHE) [3] was also proposed, where median count-based successive bisecting of sub-histograms followed by their successive sub-equalization. Also, an exposure-based sub-image HE (ESIHE) [4] was introduced by same authors, including the exposure calculation, so that on the basis of it, histogram sub-division was imparted, followed by histogram sub-equalization. However, these approaches work well for enhancement of balanced illumination images, but if histogram is not balanced, these approaches are unable to impart quality improvement because of pseudo-threshold calculation. Also, for textural and dark images, performance of these approaches is not satisfactory. Adaptive gamma correction with weighting distribution (AGCWD) [5] was also proposed for imparting contrast enhancement evaluating a gamma value set. These values in the gamma value set are evaluated by discrete cumulative distribution, which is calculated from the histogram of the input image. However, this approach fascinates many researchers [6–10] due to its adaptive nature and simplicity, but frequently leads to the saturated patches in the enhanced image, due to mapping of some already bright pixels to the saturated bright intensity level. In [11], sigmoid mapping through cosine transformed regularized-HE was also proposed, but in this approach, scaling factor calculation was not so adaptive and hence leads to lack in

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robustness. Later on, the averaging histogram equalization (AVHEQ) [12] along with the proposed frameworks like HE-based optimal profile compression (HEOPC) [13] and HE with maximum intensity coverage (HEMIC) [14] has been also proposed for quality improvement of images. These proposed frameworks were usually targeted toward gathering more and more intensity levels in the permissible range. This kind of redistribution and reallocation is found somehow inefficient, because it brings smoothening kind of ill-artifacts along with the less attention toward the textural content of the images. One more challenge which mostly remains is the unbalanced exposure and related issues. Later, in the same context, intensity and edge-based adaptive unsharp masking filter (IEAUMF) has been proposed. In IEAUMF [15], quality enhancement is suggested along with augmented image sharpening approach. HE based textural regions based enhancement approaches are also proposed [16]. In this method, histogram construction using textured gray levels only and later, this texture-based histogram is only utilized for further processing. In contrast with few advantages, this approach is incapable to give significant enhancement for smooth regions. In this chapter, a newly framed optimal fusion framework for quality image enhancement has been discussed. Fractional-order adaptive filtering has been incorporated in this framework for employing texture-adaptive or texture aware fractional-order unsharp masking. In this manner, the optimal augmentation of the sharpened fractional-order-specific adaptive filtering leads to the quality improvement of remotely sensed images. Closed-form methods [17] seem incapable to identify the extent of artifacts which has corrupted the scene because of diverse behavior of the images. Due to this reason, a closed-form approach is not so eligible for imparting on-demand adaptive quality improvement. In this manner, this issue can be identified as highly non-linear and NP-hard problem. For solving such kind of problems, optimization algorithms have played a very significant and vital role [18]. Initially, trivial suggestions of the evolutionary and population-based optimization approaches have been adopted for imparting general image enhancement. In the current scenario, it is the time to introduce the memetic inclusions in the pre-existing optimization approaches to impart swarm intelligence in a cognitive manner, so that more fruitful and next-level intelligence can be achieved for the image enhancement methods. In the contextual literature, usually contrast enhancement has been discussed in the name of quality enhancement, whereas other related issues should also be addressed for overall image quality enhancement. In this paper, a novel framework has been introduced for on-demand textural improvement of the image along with adaptive contrast enhancement. For acquiring the optimal intelligence, in this chapter memetic inclusions for CSO and SCO have been presented to demonstrate the power of the memetic intelligence in context of overall quality enhancement. Alongside the memetic intelligence, fractional-order calculus-based adaptive filtering is the key inclusion in this chapter. The texture-dependent fractional-order unsharp masking is applied for overall image quality enhancement. In this context, unsharp masking is one of the most successful ways when it is associated with fractional-order adaptive filtering along with the associated optimal intelligence. Rest part of this chapter has been planned

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accordingly as follows: Sect. 2 deals up with the proposed 2-D fractional-order unsharp masking for image enhancement, followed by the required objective function formulation in Sect. 3. Sections 4 and 5 elaborate about memetic inclusions in sine-cosine optimization algorithm and cuckoo search algorithm, respectively. Later, results and discussion are presented in Sect. 6. Finally, conclusions are drawn in Sect. 7.

2 Proposed 2-D Fractional-Order Optimal Unsharp Masking Framework for Image Enhancement For multiple band and multispectral images, multi-band processing is generally desired. Hue-Saturation-Intensity framework for decoupling non-chromatic content and chromatic content of an image is efficiently employed for corresponding color image enhancement as under: HSI ½H ðx; yÞ; Sðx; yÞ; I ðx; yÞT ¼ TRGB ½Rðx; yÞ; Gðx; yÞ; Bðx; yÞT ;

ð1Þ

HSI denotes equivalence conversion to HSI from RGB. By luminance Here, TRGB intensity channel’s isolated processing, color image quality is improved. Saturation channel and hue channel remain preserved, i.e., unchanged. On amalgamation of the fractional differentially ordered and augmented interim images as well as the statistically derived interim gamma corrected images, weighted distributed summation enhanced channel is collectively achieved as:

Iw ¼ a:Ig þ ð1  aÞ:Ifdmf ;

ð2Þ

Analytical formation and corresponding description of both interim images are as under. Gamma value set is optimally obtained from the input intensity channel’s cumulative distribution as: cðiÞ ¼ 1  ðbÞ:cdfw ðiÞ;

ð3Þ

Gamma corrected enhanced interim image can be achieved as: Ig ðiÞ ¼ ½I ðiÞcðiÞ ;

ð4Þ

A highly robust self-proposed novel fractional-order differential mask framing strategy is employed. The collective and constructive texture enhancement along with the edge restoration is also associated optimally. This approach is amalgamated with optimal gamma value set derived adaptively for overall quality enhancement through parallel pipelining by making core foundation of texture augmentation along with adaptive edge principle. For an image’s textural quality improvements, various kinds of fractional-order differential 2-D filters are

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suggested. Fifth-order differential obeying Grunwald-Letnikov (G-L) definition based on fractional calculus for f ð xÞ (where f ð xÞ 2 ½a; x; a 2 R; x 2 R) is symbolized as [19, 20]: GL v a D x f ð xÞ

n1  v X v f ðx  mhÞ; where; h!0 m m m¼0 ðvÞðv þ 1Þ. . .ðv þ m  1Þ ; ¼ m!

¼ lim hv

ð5Þ

Minimum distance between adjacent pixels is unity implying value of h also to be unity. Corresponding partial differential equations w.r.t x and y, respectively, obtained after applying the above definition over 2-D digital image is expressed as [19, 20]: Dvx f ðx; yÞ ¼

@ v f ðx; yÞ ðvÞðv þ 1Þ f ðx  2; yÞ  f ðx; yÞ þ ðvÞf ðx  1; yÞ þ @xv 2 ðvÞðv þ 1Þðv þ 2Þ Cðn  vÞ f ðx  3; yÞ þ


þ f ðx  n; yÞ; þ 6 CðvÞCðn þ 1Þ

ð6Þ Dvy f ðx; yÞ ¼

@ v f ðx; yÞ ðvÞðv þ 1Þ f ðx; y  2Þ  f ðx; yÞ þ ðvÞf ðx; y  1Þ þ v @y 2 ðvÞðv þ 1Þðv þ 2Þ Cðn  vÞ þ f ðx; y  3Þ þ


þ f ðx; y  nÞ; 6 CðvÞCðn þ 1Þ

ð7Þ By maintenance of the gradient behavior of similar fashion in all the eight directions, an FO 5  5 mask based on GL definition is created. These directions can be viewed w.r.t. the center pixel-based balanced orientation at angles of 0, 45°, 90°, 135°, 180°, 225°, 270°, 315°, and 360°, respectively. By framing of a mask of FO high pass kind, behavior of blurring filter or smoothing filter can be extended in this regard. In order to maintain sum of all the elements at unity, normalization of all these elements in this 5  5 mask is done. Various masks of size 7  7 and 3  3 are also tested, but a mask of size 5  5 has been settled as a trade-off. For detection of image’s smooth content on exclusion of minor edges and major edges, masks of integer order size are employed. In order to extract high frequency content of an image, order of FOD mask acting as 2-D adaptive filter is considered and it in turn relies on the extent of exclusion or inclusion. Edge content of an image, based on adaptive order, is to be extracted and then augmented from the input image. Highlighted textural content of an image is obtained by later emphasis on complete image. A symmetric mask is employed as in Eq. (11) by making use of first three coefficients only. Individual convolution of these filters for all rows from left to right and for all columns from top to bottom results in 2-D filtering.

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0

C3 B 0 B B FD v H55 , B 0 B @ 0 C3

0 C2 C2 C2 0

C3 C2 3 P C1  Ck k¼2 C2

C3

0 C2 C2 C2 0

1 C3 0 C C C C 3 C; C A 0 C3

ð8Þ

Care should be taken to maintain input image channel matrix size same as convolved product size. Precisely, spectral behavior of these masks helps in identification of its corresponding adaptive nature and FO changes. Input channel’s texture improved version, termed as second interim channel (Ifdmf), is derived using negatively augmented masking based on the fractional-order differentiation computation. Positively augmented version of unsharp masking mechanism is the inspiration of this analogy. By following the RL definition of optimally ordered version of FOI, emphasized texture of image is obtained. Such interim channel is given as [19]: Ifimf ¼ Iin þ k:k:Iv ;

ð9Þ

Employing GL FOD mask (H) for the input channel’s (Iin) 2-D convolutional filtering can be understood as: Iv ¼ H  Iin ;

ð10Þ

Scaling factor, k, for the adaptive augmentation is assumed to be 0.5. A hyperbolic profile, k, is adopted to accomplish design objective as under: k ¼ 0:5½1 þ tanhð3  6ðjIv j  0:5ÞÞ;

ð11Þ

Magnitude of negatively augmented filtered channel’s pixels based on FOI at corresponding image coordinates adaptive contribution decides the profile. A unity scale can be approximated as tanh(±3) = ±0.995  1 here. Rationale in defining profile based on input is also applicable here. For binding the profile coefficients within ±3, a modification is done as 6  (|Iv| −0.5) since corresponding magnitudes are bounded between ±1. Because of the textural polarities or possible two edge polarities, doubled edge magnitudes normalization accounting uses multiplication by 6. Through the weighted collective fusion of both the interim enhanced images, evaluation of an enhanced version of an image becomes possible. Over ranging resulting during evaluation of ^Ien ; must be efficiently minimized. The required optimal values for a; b; v; and g can be obtained. For framing of analytically derived search space, parametric variation is effectively given as ½a; b; v; k ½ð0; 1Þ; ð0; 1Þ; ð0; 1Þ; ð0; 2Þ:

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3 Fitness Function Formulation In the recent years, various algorithms have been proposed, and along with them, various objective functions have been also proposed for optimal image quality enhancement as shown in Table 1. In general, most of the objective functions are planned/framed and proposed by involving the idea of discrete entropy content of the image. In this chapter, the authors have framed an effective concept of amalgamation of intensity-based general discrete entropy along with GLCM-based entropy values. In this manner, both the information about intensity levels and the information about spatial co-occurrence have been considered for this purpose. The objective is to increase the value of these entropy values collectively. Along with it, the gradient magnitude (GMO) matrix’s magnitude for the output images is also included for this purpose. GMO has been evaluated by employing the Sobel-Feldman operator throughout both (row-wise and column-wise) directions. Adding all elements of the GMO tells about the extent of presence of edgy content of the image. This summation seems to be higher in magnitude and for this purpose. Its two-times logarithmic made its contribution in comparable order or comparable range. In this manner, both the edge-based and texture-based content of the image is considered. Summation of both entropies is multiplied by double-logarithmic of the summation of edges. This product is multiplied by exponential of the normalized image’s contrast measure. Thus, computed product is raised to the radical power of 1/3 and then added to the ratio of colorfulness measures of the output image w.r.t. the input image’ colorfulness measure. If this objective function is going to be employed for the enhancement of gray-scale image, then ratio of the colorfulness measures can be ignored and hence the proposed objective function can be framed for both gray-scale (uni-spectral image) and multispectral and/or color image in a dedicated manner. The mathematical expression can be written as follows:  JRGB ,

CMO CMI



vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !! u M X N u X 3 t ; ðGMO ðm; nÞÞ þ eSDO :ðDEO þ DEGLCM Þ: logðlog m¼1 n¼1

ð12Þ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !! u M X N X u 3 t SD O JGrey , e :ðDEO þ DEGLCM Þ: logðlog ; ðGMO ðm; nÞÞ m¼1 n¼1

ð13Þ

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Table 1 Various standard objective functions proposed for image enhancement S. No.

Quantitative motivation

1.

Combination of edge information and entropy [21]

2.

Peak signal to noise ratio (PSNR) [22]

Mathematical formulation   OBJ , lnfE ðIe Þg: nedgesðIe Þ M:N :H; Here, E ðIe Þ is the sum of pixel intensities of Sobel edge image ðIe Þ, nedgesðIe Þ is the count of edge pixels whose intensity value is above a threshold in ðIe Þ, H stands for entropy of the enhanced image, M and N are the count of pixels in corresponding rows and columns in the image under consideration  .  OBJ , PSNRðX; Y Þ ¼ 10 log10 ðL  1Þ2 MSEðX; Y Þ ; P  N1 PM1 2 MSEðX; Y Þ ¼ ð1=M:N Þ i¼0 j¼0 fX ði; jÞ  Y ði; jÞg ; Here, X and Y are the input and output images, respectively. Here, L is the count of discrete levels and M and N are, respectively, the count of pixels in corresponding rows and columns in the image under consideration P OBJ , Discrete Entropy ¼  Li¼1 pi log2 pi ; Here, pi is the probability of the occurrence of the ith intensity level and L is the count of discrete levels in the image

3.

Shannon entropy [23–25]

4.

Combination of FD and QILV [26]

OBJ , eFD þ eQLIV ; Here, fractal dimension (FD) and quality index-based local variance (QILV) is computed for the image under observation

5.

Combination of contrast, energy, and entropy [27]

6.

Combination of contrast and relative entropy [28] Contrast-based quality estimation (CQE) [29]

OBJ , log10 feIcon eHe =Ien g; Here, He is entropy of the enhanced image, Icon and Ien are the contrast and energy of the co-occurrence matrix   OBJ , r2 l :ðH2  H1 Þ; Here, H1 and H2 are entropy of the input and enhanced image, respectively. r2 l represents the Michelson index

7.

8.

9.

10.

Edge information, entropy, and contrast measure [30] Standard deviation, edge, and entropy [31]

Fuzzy entropy [32]

OBJ , ð1  QC =10:NB Þ; Here, NB and QC represent the total number of blocks in an image and overall contrast-based image quality OBJ , logðlogðSðIe ÞÞÞ  EðIe Þ  HðIe Þ  CðIe Þ S(Ie), E(Ie), H(Ie), and C(Ie) represent the sum of edge intensities, number of edge pixels, entropy of the image, and contrast of the image, respectively, of the enhanced image Ie pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi OBJ , 3 STD  He  MeanðjSOBELV j þ jSOBELH jÞ STD and He represent the standard deviation and entropy of the image. |SOBELV| and |SOBELH| are the images obtained by applying Sobel operators vertically and horizontally, respectively. Mean(.) indicates the averaging operator



OBJ , E 0  Q E

100

E and E′ are the original and minimized fuzzy entropy, respectively. Q is constant value. It is taken as 70 by the authors as best results had been obtained when the entropy was allowed to be reduced till 70% of its original value (continued)

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Table 1 (continued) S. No.

Quantitative motivation

Mathematical formulation

11.

Homogeneity, energy, and entropy [33]

OBJ ,

13.

Brightness, contrast, and entropy [34]

14.

Contrast, brightness, and entropy [35]

  Entropy Count of void bins  1 Homogeneity  Energy Total count of bins   X H k0 ^ : 1 L ¼ hðiÞ ¼ 0 ; k0 ¼ M:E 2 1 H represents the entropy, M represents the homogeneity, E represents the energy, and k0 represents the void bin count, respectively  2  P nov ~imn \0U~imn [ 1 ; nov ¼ OBJ , H:Dr2 : rl : 1  MN H represents the output Shannon entropy, Δr2 represents the relative contrast, r2 represents the contrast, µ represents the output brightness, respectively, for an L-bit M * N image    P 0 ^ ; k0 ¼ hðiÞ ¼ 0 OBJ , H:Dr2 : r2 l : 1  2Lk1 2 H represents the output Shannon entropy, Δr represents the relative contrast, r2 represents the contrast, µ represents the output brightness, respectively, for an L-bit M * N image

4 Memetic Inclusions for Sine-Cosine Optimizer One of the most efficient, recently proposed meta-heuristics algorithm, which has been coined as sine-cosine algorithm (SCA) [36] is discussed and employed in this chapter. The motivation behind the evolution and proposal of this optimization approach is related to the core mathematical property of the sinusoidal signals. Due to the behavioral existence and embedded involvement of sinusoidal signals in the several natural processes, it can be remarkably said that optimal solution exploration and exploitation strategy of this algorithm can be efficiently harvested for most of the image enhancement frameworks. Mostly numerical excellence of the parametric values never guarantees the enhanced visualization of the image. Hence, optimally balanced behavioral solution is in demand for this purpose. SCA is solely based on the trigonometric formulations of the conjugate behaviors of sinusoidal signals. Their conjugate and periodic relations have been explored by varying a set of governing parameters to solve the optimization problems. Both exploration and exploitation phases are highly significant for solution hunting in a feasible search space, for every optimization algorithm. These both phases are regulated in a balanced manner in SCA by following the instantaneous conjugate behavior of sine and cosine equations. In exploration part, a high finished abrupt randomness is imparted to seek out the additional and more eligible regions of the search area as a collection of random solutions. Contrary to the present, within the exploitation section, steady changes are framed for varied random solutions, and consequently, the random variations are created perceptibly less. Updating equations for both of

28

H. Singh et al.

the phases are governed by the set of equations those are inherently characterized by a related set of equations, which can be expressed as [36, 37]:



t Xi þ r1  sinðr2 Þ 

r3 Pti  Xit

; r4 \0:5; tþ1 Xi ¼ ð14Þ Xit þ r1  cosðr2 Þ  r3 Pti  Xit ; r4 0:5; In the above expression, Xit symbolizes the contemporary location for the solution which is achieved at the point of tth iteration attained in the ith dimension congruently. For the exploration part of SCA, the randomness behavior is incorporated by a set of four variables (termed as, r1 ; r2 ; r3 and r4 ) those are attaining their values in a random fashion in an equiprobable manner. All these random parameters are planned to follow the uniform probability distribution. In the above expression, r4 directs the switching among the sine rule-based updation and cosine rule-based updation. In the above expression, Pti symbolizes the contemporary location for the destination point which is to be achieved at the point of tth iteration attained in the ith dimension congruently. The above expressions are portraying in a conjugate fashion. Governing equations behave in a conjugate fashion. Also, these equations are combined by following the uniformly distributed random variable (r4 ) within the range of zero to unity. Second random parametric variable, r1, regulates the direction of the drift. This drift is solely an indistinguishable part of the updating equations. Multiple drifts of such kinds are responsible for exploration. In other words, magnitude of r1 governs the magnitude of sine and cosine expressions. Casting the entire feasible search space as a set of inward and outward regions leads to the distributed regions isolated by a circular periphery. The next parameter r2 stands for the magnitude of movement toward or away from the presumed destination. The value of parameter r3 communicates and imparts a weightage for the contribution of the destination for defining the locality of the new position of the solution. This is stochastic inclusion for obtaining the solution of the problem. The emphasized influence can be imparted by r3 [ 1; and deemphasized influence is imparted if r3 [ 1; for the net updation. The fourth parameter r4 is responsible for equiprobable switching among the sine and cosine-based conjugate equations. This counter balanced solution harvesting is drafted by the random parameter r4 : The change in the range of sine/cosine expressions forces the relative updation of the relative position, outside/inside the interim region. To follow the complete sinusoidal cycle, r2 must be varied in the feasible range of ½0; 2p: To attain the convergence, which is necessary to achieve a desired conclusion, a damping parameter is framed. For this purpose, a positive integer-based damping factor (a) is included for the evaluation of the r1 in a linearly decreasing fashion. Specifically for the tth iteration, r1 can be expressed as [36]:  r1 ¼ a 1  t=T ;

ð15Þ

In the above expression, t/T signifies the ratio of the current iteration to the total iteration count. Exploration phase of the sine-cosine optimizer is successfully

Texture-Dependent Optimal Fractional-Order …

29

governed by considering the ranges of sine and cosine functions in the range of (1, 2] & [−2, 1). Along with it, the exploitation phase is governed when the sine-cosine expressions range between −1 and 1. Saturation of the iteration count is utilized as the stopping criterion for further execution of the algorithm. Later on, various memetic inclusions have been suggested and correspondingly mimetically modified sine-cosine optimizers have been suggested as listed in Table 2.

Algorithm 1: Sine Cosine Algorithm 1: Initialize a set of search agents (solutions) 2: Repeat 3: Compute the values for all search agents by employing the proposed cost function 4: Update solution set by using the best achieved values so far (P = X ) 5: Update the random variable vector ri ( i 1 to 4) 6: Update the current search agents’ position through Eq. (19) 7: until (t < maximum iteration count) 8: Return the best found solution till the maximum count of iterations and consider it global optimum solution

Table 2 Mimetically modified sine-cosine optimizers (MMSCOs) along with ideology behind their memetic inclusions S. No.

Mimetically modified sine-cosine optimizers (MMSCOs)

Ideology behind the memetic inclusions

1.

Multi-objective sine-cosine algorithm (MOSCA) [38]

MMSCO-1

2.

Sine-cosine optimization algorithm (SCOA) [39]

MMSCO-2

3.

Binary sine-cosine algorithm (BSCA) [40]

MMSCO-3

4.

Opposition-based sine-cosine algorithm (OSCA) [41]

MMSCO-4

5.

Levy flight-based sine-cosine algorithm (LFSCA) [42]

MMSCO-5

Multi-objective SCA employs elitist non-dominated sorting approach and crowding distance approach for obtaining different non-domination levels and to preserve diversity among optimal set of solutions Sine-cosine optimization algorithm concentrates on appropriate feature selection using rounding and solves discrete variables problems and binary variables problems Binary SCA uses modified sigmoidal transformation function for binary mapping to solve binary problems Sine-cosine algorithm tends to get stuck in sub-optimal regions that are avoided using opposition-based machine learning strategy that also provides better exploration Sine-cosine algorithm gets trapped in local minima in complex non-linear optimization problems which are overcome by incorporating levy flight to update position of marked individuals (continued)

30

H. Singh et al.

Table 2 (continued) S. No.

Mimetically modified sine-cosine optimizers (MMSCOs)

Ideology behind the memetic inclusions

6.

Modified sine-cosine algorithm (MSCA) [43]

MMSCO-6

7.

Weighted sine-cosine algorithm (WSCA) [44]

MMSCO-7

8.

Improved sine-cosine algorithm (ISCA) [45]

MMSCO-8

9.

Hybrid PSO with sine-cosine acceleration coefficients [46]

MMSCO-9

10.

Improved sine-cosine algorithm (ISCA) [47]

MMSCO-10

11.

Modified sine-cosine algorithm (MSCA) [48]

MMSCO-11

12.

Binary percentile sine-cosine optimization algorithm (BPSCOA) [49]

MMSCO-12

Modified sine-cosine algorithm uses linear decreasing inertia weight parameter, exponential decreasing conversion parameter to improve precision and convergence speed. It also uses random individuals instead of optimum individuals to increase search range Position update method of sine-cosine algorithm is replaced by weighted update position mechanism based on fitness which helps converge faster Improved sine-cosine algorithm uses mutation operator to attain higher accuracy with lower sophistication Acceleration coefficients are introduced to control local search and opposition-based learning is adopted for faster convergence Improved sine-cosine algorithm integrates self-learning and global search mechanisms to reduce the overflow of diversity in search equations. It also hybridizes exploitation skills of crossover with best solutions to enhance exploitation ability Modified sine-cosine algorithm generates opposite population based on perturbation rate to jump out from local optima and self-adaptive component is added to exploit all pre visited search regions Binary percentile sine-cosine optimization algorithm applies percentile concept to solve combinatorial optimization problems

5 Memetic Inclusions in Cuckoo Search Optimizer Cuckoo search optimization [50] algorithm was designed and proposed by plagiarizing the brood parasitism behavior of cuckoos. Their aggressive breeding nature has founded the basis of exploration and exploitation for this optimization algorithm. Eggs are laid in host’s nest by this bird. By mimicking color and pattern of host’s eggs, this bird matches its eggs carefully. Either the eggs are thrown or a

Texture-Dependent Optimal Fractional-Order …

31

new nest is built by leaving the old one as and when cuckoo’s eggs are found in its nest by the host. Hence, while mimicking host’s eggs, cuckoo’s accuracy is important and while determining parasitic eggs, host’s identification skill is important in the struggle for survival. A solution is represented by every egg present in the nest, and a new solution is represented by every cuckoo’s egg in the nest, in the context of optimization. Main aim is to replace the nests not so good solutions with potentially better and new solutions. Extension of this algorithm to cases more complicated like presence of multiple eggs in each nest, representing solutions set, is also possible. Fraction P with [0, 1] probability checks if the host can discover that the eggs are not its own. On inclusion of each new iteration, current solution set will get drifted toward its new adjoining and updated solution set. This drifting for exploration and exploitation should be in a fixed and feasible search space by mimicking and planning a fixed structural neighborhood. A very fascinating fact about the CSA is that only a very few controlling parameters are required to tune, and an amateur user can also interact with it easily when compared to the other optimization algorithms. Exploration efficacy is improved by involvement of levy´ flights. In order to distinguish its eggs from that of the rest, cuckoo lays its eggs in a specific manner. Standard cuckoo search is described in a clear manner with the help of the three idealized rules as below: • Only one egg is laid at a time and dumped in a nest chosen randomly by each of the cuckoo. • Only the high-quality eggs nests are carried over to next generations as best ones. • Available host nests number is fixed and probability of discovery by host bird that the cuckoo has laid that egg is P 2 (0, 1). Host bird can abandon the nest and build a new nest or can get rid of the eggs. If possible, new nests can be replaced by the n better host nests with the help of probability P. CSOA is appreciated highly for solving multi-modality, multi-objectivity, and non-linearity optimization issues deprived of exhaustive search of any kind. In order to decide succeeding step, the current location and next state transition probability are considered. After levy distributed quasi-random flight, required intelligence is introduced and because of this step flight pattern, CSOA is profited highly. Analogous modeling can be simplified by making use of the three rules that already exists. Levy distributed flight is used for local and global exploration of the search space. Levy flight can be used for obtaining the solution xt+1 for ith cuckoo as [50]: xti þ 1 ¼ xti þ @ LevyðbÞ; where; @ [ 0;

ð16Þ

Product operation represents entry wise walk during multiplications. Levy distributed random step size is followed by random exploration as:

32

H. Singh et al.

Levy  u ¼ tk ;

8 k 2 ð1; 3;

ð17Þ

By using the random walk that is step flight distributed based on the power law, the speeding of local search occurs, as a new solution can be obtained in the complete vicinity of available solutions. Far-field randomization method is used to obtain the solutions by avoiding local trapping and encouraging global exploration. Later on, various memetic inclusions have been suggested and correspondingly mimetically modified cuckoo search optimizers have been suggested as listed in Table 3. Algorithm 2: Cuckoo Search Algorithm 1: Start 2: Objective Criterion, F(a), a=(a1,a2,…,ad)T 3: n host nests population, ai, i=1,2,…,n is initialized 4: while (ttolerance) 5: Randomly generate a cuckoo, k by making use of levy filghts 6: Its fitness or quality, fk is evaluated 7: Randomly a nest, j is chosen among n 8: if (fk>fj) 9: New solution replaces j 10: end if 11: Worst nests fraction, pa is abandoned and new nests are built at new locations via levy flights 12: Best solutions or the nests with such quality solutions are stored 13: Determine the current best by ranking the solutions 14: end while 15: Stop

Table 3 Mimetically modified cuckoo search optimizers (MMCSOs) along with ideology behind their memetic inclusions S. No.

Mimetically modified cuckoo search optimizers

Ideology behind the memetic inclusions

1.

Improved cuckoo search algorithm (ICSA) [51]

MMCSO-1

2.

Modified cuckoo search algorithm (MCSA) [52]

MMCSO-2

3.

Cuckoo search algorithm (CSA) [53]

MMCSO-3

CSA is improved to consist of multiple strategies like walking one strategy, greedy strategy, swap, and inversion strategy so that four colors can more efficiently and accurately solve planar graph coloring problem Cuckoo search algorithm is modified such that there is an addition of information exchange between best solutions and the optimization required is made free of the gradient Cuckoo search algorithm is modified so that the optimization can be achieved in an unconstrained manner by determining the step size from sorted matrix rather than permuted fitness matrix (continued)

Texture-Dependent Optimal Fractional-Order …

33

Table 3 (continued) S. No.

Mimetically modified cuckoo search optimizers

Ideology behind the memetic inclusions

4.

Modified cuckoo search with rough sets (MCSRS) [54]

MMCSO-4

5.

Modified cuckoo search algorithm (MCSA) [55]

MMCSO-5

6.

One rank cuckoo search (ORCS) [56]

MMCSO-6

7.

Adaptive cuckoo search algorithm (ACSA) [57]

MMCSO-7

8.

Discrete cuckoo search algorithm (DCSA) [58]

MMCSO-8

9.

Discrete cuckoo search algorithm (DCSA) [59]

MMCSO-9

10.

Discrete binary cuckoo search algorithm (DBCSA) [60]

MMCSO-10

11.

Cuckoo search algorithm (CSA) [61]

MMCSO-11

12.

Cuckoo search algorithm (CSA) [62]

MMCSO-12

Cuckoo search algorithm is modified to deal with high dimensionality data through the feature selection. By using the rough set theory, it builds the fitness function that takes into account the classification quality Cuckoo search algorithm is modified in a manner such that multi-objective optimization problems can be solved via two-stage solution strategy, hence improving the performance using compensating mechanism Cuckoo search algorithm is modified in a manner such that the convergence rate is improved and the exploration, exploitation phases are ranked, evaluated, combined together rather than individual as existing Cuckoo search algorithm controls different parameters with complicated constraints by using different distributions like levy, Gaussian, Cauchy as per the requirement of short-term scheduling problem Discrete cuckoo search algorithm solves the NP-hard combinatorial optimization problem of traveling salesman by overcoming the problem with cuckoo search algorithm that can solve only continuous ones Discrete cuckoo search algorithm solves the problem of traveling salesman by taking into consideration the discrete step size and the updating scheme of cuckoo A sigmoid function is used by discrete CSA to deal with the binary optimization problems overcoming the problem of cuckoo search algorithms that can deal only with continuous optimization problems Chaotic dynamics is incorporated into cuckoo search algorithm to enrich its searching behavior and also to avoid getting stuck with local optimums An improved Levenberg-Marquardt back propagation algorithm integrated and trained with CSA avoids the problems of local minima and also the problem of long time required to achieve convergence faster

34

H. Singh et al.

6 Experimentation: Performance Evaluation and Comparison 6.1

Assessment Criterion

Various state-of-the-art approaches have been listed and exercised for the comparative evaluation of their performance with the sole objective of quality improvement of the remotely sensed images. In this context, parallel/vertical comparative evaluation has been presented by employing the state-of-the-art methods like, GHE [63], MMSICHE [3], BPFDHE [1], AGCWD [5], RHE-DCT [11], AVHEQ [12], HEOPC [13], HEMIC [14], IEAUMF [15], DOTHE [16], and the proposed approach. A set of primary quality performance measures is evaluated for this experimentation, namely the brightness content, contrast or variance of the image, discrete entropy content of the image, gradient or the sharpness content of the image, and the colorfulness measure of the image. Value of brightness (B) or mean for an image I(m, n) of size M by N, expressed as an average summation, is given as: Brightness ðBÞ ¼

M X N 1 X Iðm; nÞ; M  N m¼1 n¼1

ð18Þ

Contrast (V) of an image, responsible for its naturally pleasant look, is accounted due to the variance or the average intensity spread and is given as: 1 X ContrastðV Þ ¼ Iðm; nÞ2  M  N m;n

1 X Iðm; nÞ M  N m;n

!2 ;

ð19Þ

Shannon entropy quantifies the information content of an image. By making use of normalized image histogram, bounded probability calculation is given as: EntropyðE Þ ¼ 

Imax X

pi log2 ðpi Þ;

ð20Þ

i¼0

where pi ¼ ni =ðM  N Þ represents the possibility of occurrence in accordance with the intensity level and, Imax represents the maximum intensity level. Sharpness or gradient of an image helps in identification of the edge content of the image and is given as: SharpnessðSÞ ¼

ffi 1 X pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dm2 þ Dn2 ; M  N m;n

ð21Þ

Texture-Dependent Optimal Fractional-Order …

35

where Dx ¼ Ienh ðm; nÞ  Ienh ðm þ 1; nÞ and Dy ¼ Ienh ðm; nÞ  Ienh ðm; n þ 1Þ represents the local values of the gradient of an image. Color channel’s coordination is noteworthy in case of color images. Hence, colorfulness of the image can be termed as the coordination among various color channels by exploiting relative colors’

Input

GHE

MMSICHE

BPFDHE

AGCWD

RHE-DCT

AVHEQ

HEOPC

HEMIC

IEAUMF

DOTHE

Proposed

Fig. 1 Visual evaluation and comparison for Image S. No. 1

36

H. Singh et al.

mean and relative colors’ variance. Mathematically, the colorfulness content is identified as: ColorfulnessðCÞ ¼ Drg ¼ R  G;

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r2rg þ r2yb þ 0:3 l2rg þ l2yb ; Dyb ¼ 0:5ðR þ GÞ  B;

ð22Þ ð23Þ

where Drg ; Dyb ; lrg ; lyb ; rrg ; ryb ; represents the differential values, mean, and standard deviation, respectively.

Input

GHE

MMSICHE

BPFDHE

AGCWD

RHE-DCT

AVHEQ

HEOPC

HEMIC

IEAUMF

DOTHE

Proposed

Fig. 2 Visual evaluation and comparison for Image S. No. 2

Texture-Dependent Optimal Fractional-Order …

6.2

37

Qualitative Assessments

Comparative qualitative evaluation with recently published state-of-the-art methodologies (namely GHE [63], MMSICHE [3], BPFDHE [1], AGCWD [5], RHE-DCT [11], AVHEQ [12], HEOPC [13], HEMIC [14], IEAUMF [15], DOTHE [16], and the proposed approach) is presented in Figs. 1, 2, and 3, for highlighting the significant contribution by employing experimentation for test images [64–66] .

6.3

Quantitative Assessments

For explicit comparative numerical assessments, relevant performance indices are listed in Table 4. Relative comparisons with other population-based optimization algorithms for similar iteration counts have been presented in Table 5. For this purpose, various algorithms like genetic algorithm (GA), simulated annealing (SA),

Input

GHE

MMSICHE

BPFDHE

AGCWD

RHE-DCT

AVHEQ

HEOPC

HEMIC

IEAUMF

DOTHE

Proposed

Fig. 3 Visual evaluation and comparison for Image S. No. 3

B V H S C B V H S C B V H S C

1.

3.

2.

Indices

S. No.

0.143 0.023 0.0525 4.6992 0.1426 0.252 0.0475 0.0673 6.2499 0.2839 0.1265 0.022 0.0551 4.9446 0.09

INPUT

0.5382 0.06 0.0925 5.4557 0.5829 0.5259 0.0669 0.08 6.5434 0.4493 0.5684 0.0458 0.0816 5.4939 0.4987

GHE

0.1852 0.0552 0.0745 5.2727 0.1452 0.2959 0.0828 0.0871 6.6297 0.0915 0.1833 0.0653 0.089 5.4127 0.0896

MMISCHE 0.3079 0.0734 0.108 5.6113 0.4494 0.3711 0.0897 0.0984 6.7393 0.3813 0.2637 0.0696 0.102 5.6706 0.3462

BPFDHE 0.1925 0.042 0.0616 5.2968 0.1519 0.3461 0.0918 0.0935 6.5505 0.1122 0.1719 0.0423 0.0761 5.1541 0.0855

AVHEQ 0.2657 0.0604 0.0917 4.8846 0.2287 0.348 0.0733 0.0832 6.2553 0.1206 0.2393 0.0623 0.0949 5.2182 0.1259

RHE-DCT 0.1708 0.0309 0.0592 5.2115 0.1332 0.3486 0.0887 0.0921 6.611 0.1126 0.1761 0.0409 0.0751 5.3898 0.0872

AGCWD 0.2944 0.0416 0.0703 6.0543 0.2189 0.3679 0.0857 0.0905 6.9469 0.1167 0.3776 0.0307 0.069 5.9595 0.2064

HEOPC 0.2093 0.0538 0.0932 5.4682 0.167 0.349 0.0912 0.099 6.5278 0.1134 0.1871 0.0573 0.1146 5.2632 0.0966

HEMIC 0.2378 0.0478 0.0901 5.066 0.2472 0.3758 0.0872 0.102 6.6429 0.381 0.2116 0.0488 0.0923 5.3315 0.2615

IEAUMF

0.2923 0.0652 0.0991 5.1014 0.2425 0.3898 0.0936 0.0963 6.6238 0.1318 0.2537 0.0639 0.0986 5.3785 0.1335

DOTHE

0.4194 0.0907 0.1864 6.3343 0.5583 0.4515 0.094 0.155 7.4462 0.4319 0.3756 0.0872 0.1573 6.7118 0.4191

Proposed

Table 4 Quantitative evaluation with comparison among input images, GHE [63], MMSICHE [2], BPFDHE [1], AVHEQ [7], AGCWD [5], HEOPC [8], HEMIC [9], IEAUMF [10], ETHE [11], DOTHE [11], and the proposed approach using various metrics termed as brightness (B), contrast (V), entropy (H), sharpness (S), and colorfulness (C)

38 H. Singh et al.

B V H S C B V H S C B V H S C

1.

3.

2.

Indices

S. No.

0.3976 0.0860 0.1767 6.0049 0.5293 0.4280 0.0891 0.1469 7.0590 0.4094 0.3561 0.0827 0.1491 6.3628 0.3973

GA

0.3984 0.0862 0.1771 6.0176 0.5304 0.4289 0.0893 0.1473 7.0739 0.4103 0.3568 0.0828 0.1494 6.3762 0.3981

SA 0.3993 0.0863 0.1775 6.0303 0.5315 0.4298 0.0895 0.1476 7.0888 0.4112 0.3576 0.0830 0.1497 6.3896 0.3990

ACO 0.4001 0.0865 0.1778 6.0429 0.5326 0.4307 0.0897 0.1479 7.1037 0.4120 0.3583 0.0832 0.1501 6.4031 0.3998

PSO 0.4009 0.0867 0.1782 6.0556 0.5337 0.4316 0.0899 0.1482 7.1186 0.4129 0.3591 0.0834 0.1504 6.4165 0.4007

DE 0.4018 0.0869 0.1786 6.0683 0.5349 0.4325 0.0901 0.1485 7.1335 0.4138 0.3598 0.0835 0.1507 6.4299 0.4015

BFO 0.4026 0.0871 0.1789 6.0809 0.5360 0.4334 0.0902 0.1488 7.1484 0.4146 0.3606 0.0837 0.1510 6.4433 0.4023

HS 0.4035 0.0873 0.1793 6.0936 0.5371 0.4343 0.0904 0.1491 7.1632 0.4155 0.3613 0.0839 0.1513 6.4568 0.4032

ABC 0.4043 0.0874 0.1797 6.1063 0.5382 0.4352 0.0906 0.1494 7.1781 0.4164 0.3621 0.0841 0.1516 6.4702 0.4040

FA

Table 5 Quantitative comparison among swarm or population-based optimization models [57] for the proposed approach 0.4051 0.0876 0.1801 6.1189 0.5393 0.4361 0.0908 0.1497 7.1930 0.4172 0.3628 0.0842 0.1520 6.4836 0.4049

BA

0.4060 0.0878 0.1804 6.1316 0.5404 0.4371 0.0910 0.1500 7.2079 0.4181 0.3636 0.0844 0.1523 6.4970 0.4057

CS

0.4194 0.0907 0.1864 6.3343 0.5583 0.4515 0.0940 0.1550 7.4462 0.4319 0.3756 0.0872 0.1573 6.7118 0.4191

SCA

Texture-Dependent Optimal Fractional-Order … 39

3.

0.0846

0.1526

6.5104

0.4065

0.0844

0.1523

6.4970

0.4057

V

H

S

C

0.4189

0.4181

C

0.3643

7.2228

7.2079

S

0.3636

0.1504

0.1500

H

B

0.0912

0.0910

V

0.5416

0.5404

C

0.4380

6.1443

6.1316

S

0.4371

0.1808

0.1804

H

B

0.0880

0.0878

V

2.

0.4068

0.4060

B

MMSCO-2 [39]

1..

MMSCO-1 [38]

Indices

S. No.

0.4074

6.5239

0.1529

0.0848

0.3651

0.4198

7.2377

0.1507

0.0914

0.4389

0.5427

6.1569

0.1812

0.0882

0.4077

MMSCO-3 [40]

0.4082

6.5373

0.1532

0.0849

0.3658

0.4207

7.2526

0.1510

0.0916

0.4398

0.5438

6.1696

0.1816

0.0883

0.4085

MMSCO-4 [41]

0.4090

6.5507

0.1535

0.0851

0.3666

0.4215

7.2675

0.1513

0.0917

0.4407

0.5449

6.1823

0.1819

0.0885

0.4093

MMSCO-5 [42]

0.4099

6.5641

0.1538

0.0853

0.3673

0.4224

7.2824

0.1516

0.0919

0.4416

0.5460

6.1949

0.1823

0.0887

0.4102

MMSCO-6 [43]

0.4107

6.5776

0.1542

0.0855

0.3681

0.4233

7.2973

0.1519

0.0921

0.4425

0.5471

6.2076

0.1827

0.0889

0.4110

MMSCO-7 [44]

0.4116

6.5910

0.1545

0.0856

0.368

0.4241

7.3122

0.1522

0.0923

0.4434

0.5483

6.2203

0.1830

0.0891

0.4119

MMSCO-8 [45]

0.4082

6.5373

0.1532

0.0849

0.3658

0.4207

7.2526

0.1510

0.0916

0.4398

0.5438

6.1696

0.1816

0.0883

0.4085

MMSCO-9 [46]

0.4132

6.6178

0.1551

0.0860

0.3703

0.4259

7.3420

0.1528

0.0927

0.4452

0.5505

6.2456

0.1838

0.0894

0.4135

MMSCO-10 [47]

0.4141

6.6313

0.1554

0.0862

0.3711

0.4267

7.3568

0.1531

0.0929

0.4461

0.5516

6.2583

0.1842

0.0896

0.4144

MMSCO-11 [48]

Table 6 Quantitative comparison among various mimetically modified sine-cosine optimizers (MMSCOs) for the proposed approach

0.4191

6.7118

0.1573

0.0872

0.3756

0.4319

7.4462

0.1550

0.0940

0.4515

0.5583

6.3343

0.1864

0.0907

0.4194

MMSCO-12 [49]

40 H. Singh et al.

3.

0.0845

0.1524

6.5037

0.4061

0.0843

0.1521

6.4903

0.4053

V

H

S

C

0.4185

0.4176

C

0.3640

7.2154

7.2005

S

0.3632

0.1502

0.1499

H

B

0.0911

0.0909

V

0.5410

0.5399

C

0.4375

6.1379

6.1253

S

0.4366

0.1806

0.1802

H

B

0.0879

0.0877

V

2.

0.4064

0.4056

B

MMCSO-2 [52]

1.

MMCSO-1 [51]

Indices

S. No.

0.4069

6.5172

0.1527

0.0847

0.3647

0.4194

7.2303

0.1505

0.0913

0.4384

0.5421

6.1506

0.1810

0.0881

0.4072

MMCSO-3 [53]

0.4078

6.5306

0.1531

0.0848

0.3655

0.4202

7.2452

0.1508

0.0915

0.4393

0.5432

6.1633

0.1814

0.0883

0.4081

MMCSO-4 [54]

0.4086

6.5440

0.1534

0.0850

0.3662

0.4211

7.2600

0.1511

0.0917

0.4402

0.5443

6.1759

0.1817

0.0884

0.4089

MMCSO-5 [55]

0.4095

6.5574

0.1537

0.0852

0.3670

0.4220

7.2749

0.1514

0.0918

0.4411

0.5455

6.1886

0.1821

0.0886

0.4098

MMCSO-6 [56]

0.4103

6.5709

0.1540

0.0854

0.3677

0.4228

7.2898

0.1517

0.0920

0.4420

0.5466

6.2013

0.1825

0.0888

0.4106

MMCSO-7 [57]

0.4111

6.5843

0.1543

0.0855

0.3685

0.4237

7.3047

0.1521

0.0922

0.4429

0.5477

6.2139

0.1829

0.0890

0.4114

MMCSO-8 [58]

0.4120

6.5977

0.1546

0.0857

0.3692

0.4246

7.3196

0.1524

0.0924

0.4438

0.5488

6.2266

0.1832

0.0892

0.4123

MMCSO-9 [59]

0.4128

6.6111

0.1549

0.0859

0.3700

0.4254

7.3345

0.1527

0.0926

0.4447

0.5499

6.2393

0.1836

0.0893

0.4131

MMCSO-10 [60]

0.4137

6.6245

0.1553

0.0861

0.3707

0.4263

7.3494

0.1530

0.0928

0.4456

0.5510

6.2520

0.1840

0.0895

0.4139

MMCSO-11 [61]

Table 7 Quantitative comparison among various mimetically modified cuckoo search optimizers (MMCSO) for the proposed approach

0.4191

6.7118

0.1573

0.0872

0.3756

0.4319

7.4462

0.1550

0.0940

0.4515

0.5583

6.3343

0.1864

0.0907

0.4194

MMCSO-12 [62]

Texture-Dependent Optimal Fractional-Order … 41

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ant colony optimization algorithm (ACO), particle swarm optimization (PSO), differential evolution (DE), bacterial foraging optimization (BFO), harmony search (HS), artificial bee colony (ABC) algorithm, firefly algorithm (FA), bat algorithm (BA), cuckoo search (CS), and sin-cosine algorithm (SCA) have been employed for comparative experimentation. Relative comparisons for mimetically modified cuckoo search optimizers (MMSCOs) and mimetically modified sine-cosine optimizers (MMSCOs) for the proposed approach for relevant indices are listed in Tables 6 and 7.

7 Conclusion The proposed optimally ordered fractional differentiation based filtering is established as an efficient image quality enhancement method in this chapter. Also, a very rigorous experimentation has been performed in this chapter by employing the performance evaluation and comparison with pre-existing recently proposed and highly appreciated quality enhancement approaches. Most significantly, the memetic intelligence has been also explored in this chapter. A series of memetic inclusions for SCO and CSO have been tested and identified well in the collaborative fashion for this dedicated purpose of overall quality improvement. Various kinds of objective functions have been tested for this purpose. Also, a newly derived objective function and its formulation are also discussed which works positively along with the proposed optimal fractional-order framework. Thus, the whole proposed framework will be beneficial for information harvesting through airborne remotely sensed dark satellite images, acquired under poor illumination.

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7. Singh, H., Agrawal, N., Kumar, A., Singh, G.K., Lee, H.N.: A novel gamma correction approach using optimally clipped sub-equalization for dark image enhancement. In: 21st IEEE International Conference on Digital Signal Processing (DSP), Beijing, China, pp. 497– 501 (2016) 8. Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: A novel optimally gamma corrected intensity span maximization approach for dark image enhancement. In: 22nd IEEE International Conference on Digital Signal Processing (DSP), London, United Kingdom, pp. 1–5 (2017) 9. Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Regionally equalized and contextually clipped gamma correction approach for dark image enhancement. In: 4th IEEE International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp. 431–436 (2017) 10. Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Dark image enhancement using optimally compressed and equalized profile based parallel gamma correction. In: 6th IEEE International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 1299– 1303 (2017) 11. Fu, X., Wang, J., Zeng, D., Huang, Y., Ding, X.: Remote sensing image enhancement using regularized histogram equalization and DCT. IEEE Geosci. Remote Sens. Lett. 12(11), 2301– 2305 (2015) 12. Lin, S.C.F., Wong, C.Y., Rahman, M.A., Jiang, G., Liu, S., Kwok, N.: Image enhancement using the averaging histogram equalization approach for contrast improvement and brightness preservation. Comput. Electr. Eng. 46, 356–370 (2014) 13. Wong, C.Y., Jiang, G., Rahman, M.A., Liu, S., Lin, S.C.F., Kwok, N., et al.: Histogram equalization and optimal profile compression based approach for color image enhancement. J. Vis. Commun. Image Represent. 38, 802–813 (2016) 14. Wong, C.Y., Liu, S., Liu, S.C., Rahman, M.A., Lin, S.C.F., Jiang, G., et.al.: Image contrast enhancement using histogram equalization with maximum intensity coverage. J. Modern Opt. 63(16), 1618–1629 15. Lin, S.C.F., Wong, C.Y., Jiang, G., Rahman, M.A., Ren, T.R., Kwok, N., et al.: Intensity and edge based adaptive unsharp masking filter for color image enhancement. Optik Int. J. Light Electr. Opt. 127(1), 407–414 (2016) 16. Singh, K., Vishwakarma, D.K., Walia, G.S., Kapoor, R.: Contrast enhancement via texture region based histogram equalization. J. Mod. Opt. 63(15), 1444–1450 (2016) 17. Singh, H., Kumar, A., Balyan, L.K., Lee, H.N.: Optimally sectioned and successively reconstructed histogram sub-equalization based gamma correction for satellite image enhancement. Multimed. Tools Appl. 78(14), 20431–20463 (2019) 18. Hemanth, J., Balas, V.E.: Nature Inspired Optimization Techniques for Image Processing Applications. Springer International Publishing, Berlin (2019) 19. Singh, H., Kumar, A., Balyan, L.K., Lee, H.N.: Fractional order integration based fusion model for piecewise gamma correction along with textural improvement for satellite images. IEEE Access 7, 37192–37210 (2019) 20. Singh, H., Kumar, A., Balyan, L.K., Lee, H.N.: Piecewise gamma corrected optimally framed Grumwald-Letnikov fractional differential masking for satellite image enhancement. In: 7th IEEE International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 0129–0133 (2018) 21. Mohan, S., Mahesh, T.R.: Particle swarm optimization based contrast limited enhancement for mammogram images. In: International Conference on Intelligent Systems and Control, pp. 384–388 (2013) 22. Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Slantlet filter-bank based satellite image enhancement using gamma corrected knee transformation. Int. J. Electron. 105(10), 1695– 1715 (2018) 23. Shanmugavadivu, P., Balasubramanian, K., Muruganandam, A.: Particle swarm optimized bi-histogram equalization for contrast enhancement and brightness preservation of images. J. Vis. Comput. 30(4), 387–399 (2014)

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24. Wan, M., Gu, G., Qian, W., Ren, K., Chen, Q., Maldague, X.: Particle swarm optimization based local entropy weighted histogram equalization for infrared image enhancement. Infrared Phys. Technol. 91, 164–181 (2018) 25. Babu, P., Rajamani, V.: Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images. Int. J. Imaging Syst. Technol. 25(1), 24–32 (2015) 26. Dhal, K.G., Das, S.: Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement. J. Pattern Recognit. Image Anal. 27(4), 695–712 (2017) 27. Dhal, K.G., Das, S.: Local search based dynamically adapted Bat Algorithm in image enhancement domain. Int. J. Comput. Sci. Math. (2017) 28. Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement. Comput. Electr. Eng. 70, 462–475 (2018) 29. Joshi, P., Prakash, S.: An efficient technique for image contrast enhancement using artificial bee colony. In: International Conference on Identity, Security and Behavior Analysis, pp. 1–6 (2015) 30. Chen, J., Yu, W., Tian, J., Chen, L., Zhou, Z.: Image contrast enhancement using an artificial bee colony algorithm. J. Swarm Evolut. Comput. 38, 287–294 (2017) 31. Hoseini, P., Shayesteh, M.G.: Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. J. Digit. Signal Process. 23 (3), 879–893 (2013) 32. Verma, O.P., Chopra, R.R., Gupta, A.: An adaptive bacterial foraging algorithm for color image enhancement. In: Annual Conference on Information Science and Systems, pp. 1–6 (2016) 33. Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: A new optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Comput. Electr. Eng. 75, 245–261 (2019) 34. Singh, H., Kumar, A., Balyan, L.K.: A levy flight firefly optimizer based piecewise gamma corrected unsharp masking framework for satellite image enhancement. In: 14th IEEE India Council International Conference (INDICON), Roorkee, India, pp. 1–6 (2017) 35. Singh, H., Kumar, A., Balyan, L.K.: Cuckoo search optimizer based piecewise gamma corrected auto clipped tile wise equalization for satellite image enhancement. In: 14th IEEE India Council International Conference (INDICON), Roorkee, India, pp. 1–5 (2017) 36. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016) 37. Singh, H., Kumar, A., Balyan, L.K.: A sine-cosine optimizer-based gamma corrected adaptive fractional differential masking for satellite image enhancement. In: Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol. 741, pp. 633–645. Springer, Singapore (2019) 38. Tawhid, M.A., Savsani, V.: Multi objective sine cosine algorithm for multi objective engineering design problems. J. Neural Comput. Appl. 31(2), 915–929 (2019) 39. Hafez, A.I., Zawbaa, H.M., Emary, E., Hassanien, A.E.: Sine cosine optimization algorithm for feature selection. In: International Symposium on Innovations in Intelligent Systems and Applications, pp. 1–5 (2016) 40. Reddy, K.S., Panwar, L.K., Panigrahi, B.K., Kumar, R.: A new binary variant of sine cosine algorithm: development and application to solve profit based unit commitment problem. Arab J. Sci. Eng. 43, 4041–4056 (2018) 41. Elaziz, M.A., Oliva, D., Xiong, S.: An improved opposition based sine cosine algorithm for global optimization. J. Exp. Syst. Appl. 90, 484–500 (2017) 42. Ning, L., Gang, L., Liang, D.Z.: An improved sine cosine algorithm based on levy flight. Int. Conf. Dig. Image Proces. 10420(104204R), 1–6 (2017) 43. Qu, C., Zeng, Z., Dai, J., Yi, Z., He, W.: A modified sine cosine algorithm based on neighborhood search and greedy levy mutation. J. Comput. Intell. Neurosci. 2, 1–19 (2018)

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Artificial Bee Colony: Theory, Literature Review, and Application in Image Segmentation Emrah Hancer

Abstract The artificial bee colony (ABC) is one of the most well-regarded swarm intelligence-based algorithms in the literature of evolutionary computation techniques. This algorithm mimics the foraging behaviors of bees in the hive. Due to its well-designed search ability and dependency on fewer control parameters, ABC has extensive usage in various fields. One of the remarkable areas where ABC has been successfully implemented is the image segmentation. Image segmentation is the process of dividing a digital image into multiple segments. The overall goal of image segmentation is to extract meaningful information from an image. Although considerable attempts have been made to develop image segmentation techniques using ABC, it is not possible to find any work in the literature that particularly seeks to reflect the profile of ABC-based image segmentation techniques. This chapter first tries to describe ABC-based image segmentation techniques from the fundamental concepts of segmentation such as clustering, thresholding, and edge detection. The chapter also applies ABC algorithm to a challenging task in image segmentation. It is observed that ABC can accurately segment the regions of an image.










Keywords Artificial bee colony Image segmentation Clustering Thresholding

1 Introduction A digital image is a common way of transferring a variety of useful information. Besides useful information, the image also involves unnecessary and noisy information. Thus, the process of analyzing the image and extracting important knowledge from the image plays a crucial role to achieve satisfactory results in various fields, such as medical applications, remote sensing, face recognition, and E. Hancer (&) Department of Computer Technology and Information Systems, Mehmet Akif Ersoy University, Burdur, Turkey e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_3

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texture analysis, to name a few. From the perspective of image understanding and analysis, image segmentation is a fundamental tool that is used to partition the image into several regions or categories based on certain criteria. Each region of a segmented image corresponds to a different object or a different part of an object. To be treated as a successful segmentation process, a segmented image typically should have the following properties: • Pixels within the same region have similar multivariate gray-scale values and form a connected component. • Pixels between different regions have dissimilar gray-scale values. Let us consider a digital scanned topographic image (Fig. 1) to reflect the critical role of segmentation. A topographical map involves a variety of topographical and geographical individuals such as forest cover, buildings, water supplies, roads, contour lines, etc., to describe and illustrate a considered region in the world clearly. Each has its own characteristics. For instance, water supplies are generally represented via blue pixel values, and contour lines are represented via brown pixel values. The extraction and reconstruction processes of contour lines from topographic maps are an exciting research area [1–3]. Image segmentation is a fundamental preprocessing step used to distinguish such features represented in topographic maps. If segmentation is well done, further steps such as extraction and reconstruction processes will be simpler without no doubt. Otherwise, lots of gaps will be observed on the contour lines, and thereby the reconstruction process will become more time-consuming and more complex. A variety of approaches have been used in the literature to carry out image segmentation. The approaches used for segmentation are fundamentally categorized as follows.

Fig. 1 An example for topographic map

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49

• Thresholding: Each pixel in the image is allocated to the range of threshold values that are determined based on a certain criterion, such as histogram-based, entropy-based, and attribute-based. The assigned pixels within each threshold value represent a region of the image concerning the object or background in which we are interested or not. • Edge-based: Each pixel in the image is divided as edge or non-edge using a pre-specified edge filter, such as Prewitt and Sobel. Typically, such filters try to detect discontinuities in brightness. • Clustering-based: Each pixel in the image is allocated to the group of clusters depending on a specified criterion, such as distance-based, density-based, and Gaussian mixture models. Typically, a clustering algorithm should provide maximum homogeneity within the cluster and maximum heterogeneity between clusters. • Learning-based: One of the novel ideas is to build a supervised model to classify objects in the image dataset using convolutional neural networks (CNN). The usage of CNN and its variants in image segmentation has been widely growing up, especially in recent years. While lots of approaches can be found in the literature to deal with segmentation tasks, they are dependent on some specific parameters to be optimally tuned to achieve great segmentation performance and thereby these methods can be considered as candidate optimization problems. Furthermore, assumptions in such methods sometimes cause challenges to be adopted in traditional optimization methods. Therefore, alternative techniques which do not require assumptions are required. Evolutionary computation techniques are well-known global optimization algorithms to address such issues. Evolutionary computation techniques are investigated in two categories, which are evolutionary algorithms and swarm intelligence algorithms. Evolutionary algorithms are modeled by mimicking the principles of genetic inheritance phenomena. Genetic algorithms (GA) [4], genetic programming (GP) [5], evolutionary strategies (ES) [6], and differential evolution (DE) [7] are some widely used examples of this category. The other category of evolutionary computation, swarm intelligence algorithms are simulated by the behaviors of self-organizing agents such as fish schooling, birds flocking, etc. Ant colony optimization (ACO) [8], particle swarm optimization (PSO) [9], artificial bee colony (ABC) [10], and bacterial foraging optimization (BFO) [7] can be given as successful examples of this category. Proposed by Karaboga in [11], the artificial bee colony (ABC) algorithm mimics the behaviors of foraging behaviors of honey bees in the hive. Compared to other evolutionary techniques, it requires fewer control parameters, is easy to implement and has a well-designed balance between exploitation and exploration. Due to such advantages, ABC has been successfully applied to a variety of single objective and multi-objective, continuous and combinatorial problems [12–14] since it was invented in 2005. Moreover, several surveys concerning ABC and its applications have been published to thoroughly reflect the profile of ABC algorithm [15–17]. Although a variety of successful applications have been developed using ABC for

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image segmentation, it is not possible to find a study that explicitly investigates and reviews the profile of ABC in this field. To alleviate this issue, we survey the applications of ABC proposed for image segmentation with a brief discussion in this chapter. This chapter aims to attract the interest of experts in image segmentation to pay much attention to developing segmentation methodologies wrapped around ABC algorithm. In the rest of this chapter, we first briefly explain image segmentation and ABC algorithm. Then, we review and discuss ABC-based segmentation methodologies. After that, we present an ABC-based segmentation methodology to extract brain tumors from MRI images. Lastly, we provide a conclusion section with current drawbacks and future trends on image segmentation.

2 Background 2.1

Image Segmentation

In this section, we explain the most common approaches used to segment images into regions.

2.1.1

Thresholding

Treated as the simplest and most common approach used for image segmentation, thresholding is to simply categorize each pixel of an image at position (x, y) with intensity value Ixy by transforming its intensity value into a binary value. For a given single threshold value T, if T\Ixy ; Ixy is labeled as zero (0) and so is allocated to the category of background; otherwise, it is labeled as one (1) and so is allocated to the category of interested objects. This type of thresholding is referred as bi-level thresholding. More than one threshold value can be used to produce more than two categories. This type of thresholding is referred to as multilevel thresholding. For an image (Im) representing with L gray levels, multilevel thresholding can be defined as follows. R0 ¼ fI ðx; yÞ 2 Im j 0  I ðx; yÞ  T1 1g R1 ¼ fI ðx; yÞ 2 Im j T1  I ðx; yÞ  T2 1g R2 ¼ fI ðx; yÞ 2 Im j T2  I ðx; yÞ  T2 1g

ð1Þ

Rm ¼ fI ðx; yÞ 2 Im j Tm  I ðx; yÞ  Tm 1g where m is the number of thresholds and Tm is the mth threshold. One of the well-known thresholding criteria, Kapur’s entropy [18] aims to find the optimal thresholds by maximizing entropy between the classes which represents

Artificial Bee Colony: Theory, Literature Review, …

51

the regions between thresholds. Assume that T = {t1, t2, …, tn} is a vector of thresholds for an image, the Kapur’s entropy is calculated as follows: H ðt1 ; t2 ; . . .; tn Þ ¼ H0 þ H1 þ


þ Hn

ð2Þ

where H0, H1, Hn represents the entropies of classes:   t1 1 X pj pj ln w w 0 j¼0 0   tX 2 1 pj pj ln H1 ¼  w w0 j¼t1 0   L1 X pj pj Hn ¼  ln w w0 j¼tn 0

H0 ¼ 

ð3Þ

where pj is the probability distribution of the intensity levels, and wn is the probability occurrence for the nth class. Another popular thresholding algorithm, Otsu [19] tries to define a global optimum threshold value by maximizing the inter-class variance (Eq. 4). Otsu is simple to implement and a quite fast algorithm; however, it assumes the histogram of an image is normally distributed. In other words, its performance is negatively affected by noise and gray-scale unevenness. Moreover, it does not consider the spatial information of an image. Other well-known thresholding approaches are co-occurrence matrix, entropy-based, contextual-based, and histogram-based. r2B ¼

n X

 2 wj lj  lr

ð4Þ

j¼1

where wj is the probability occurrence for the jth class, lj is the mean of the jth class, and lr is the total mean of the image.

2.1.2

Edge-Based Segmentation

The edge of an object in an image is present in the form of discontinuities. The sharp changes in brightness and texture can be given as examples of the discontinuities in an image. Such discontinuities are used to detect edges so as to segment an image into objects. For instance, a sharp change (boundary) can be observed between two adjacent regions with different intensity values in an image. The most common way to detect such discontinuities is to use derivative operations. We also provide an example in Fig. 2 to visualize how to detect sharpness between adjacent regions using derivative operations. The first-order and second-order derivatives can be defined by Eqs. (5) and (6) in terms of one dimension. While the first-order

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Fig. 2 An illustrative example of discontinuities in image [59]

derivatives result thicker edges than the second-order derivatives, the second-order derivatives can detect fine details, such as thin lines and isolated pixels. The second-order derivatives are more general than the first-order derivatives for image segmentation thanks to providing more details. The representative examples of the approaches wrapped around the second-order derivatives are the Laplacian and Sobel filters.

2.1.3

@f ¼ f ð x þ 1Þ  f ð x Þ @x

ð5Þ

@f ¼ f ðx þ 1Þ þ f ðx  1Þ  2f ð xÞ @x

ð6Þ

Clustering-Based Segmentation

Another most commonly used approach in image segmentation is the clustering which is the process of allocating pixels of an image into clusters by evaluating the similarities between pixels using some predefined criteria, such as distance, interval, and density. Clustering methods can be divided into hierarchical, partitional, model-based, density-based, and region-based [20]. Among such clustering methods, hierarchical and partitional algorithms are the most commonly preferred ones due to their historical background. Hierarchical algorithms typically build a dendrogram (the cluster tree) by splitting (referred to as divisive) or merging (referred to as agglomerative) clusters. From the perspective of agglomerative algorithms, a dendrogram is built by starting from several clusters, each of which represents a pixel in the image, and then iteratively merging into bigger clusters based on some predefined criteria, such as single-linkage and complete-linkage. In contrast to agglomerative algorithms, hierarchical algorithms start from a cluster involving all instances of the considered image and then build a dendrogram by splitting into subclusters. After the creation of the cluster tree, a cluster level is chosen. However, it is not easy to determine which level is the most suitable to represent the input image.

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The overall goal of partitional algorithms is to minimize the distance within clusters and maximize the separation between clusters. In other words, the generated clusters by a partitional algorithm should provide maximum homogeneity within clusters and maximum heterogeneity between clusters. In contrast to hierarchical algorithms, partitional algorithms are more preferred than hierarchical algorithms in the applications of image analysis and pattern recognition. The most representative partitional algorithm is the K-means algorithm [21], which iteratively tries to optimize the Euclidean distance within clusters. Due to the characteristics of K-means, it can also be considered as an optimization problem. K-means is simple to implement and computationally efficient algorithm but depends on initial conditions and the characteristics of the considered problem. Another most commonly used one is fuzzy c means (FCM) [22], which is typically treated as the fuzzy version of K-means. Compared to K-means, FCM works well in noisy images. However, initial conditions also affect the performance of FCM. What are the initial conditions affecting the performance of partitional algorithms? For instance, if we select initial centroids or fuzzy memberships according to the characteristics of the data problem, the performance of a partitional algorithm will be increased; otherwise, its performance will be worse.

2.1.4

Learning-Based Segmentation

Deep learning has recently been introduced as a novel technique of machine learning based on the self-learning principles of humans. Deep learning has become a key technology in several fields, such as image classification, segmentation, and detection. Moreover, the results which were not possible to be achieved in the past are now possible to be obtained using deep learning. A learning model is typically built on a set of data with the labeled information and neural network systems with many layers. One of the crucial tasks with which deep learning concerns is image semantic segmentation. Semantic segmentation is the process of categorizing each pixel of an image into an object or a part of the image. By this way, semantic segmentation is a fundamental part of scene understanding such that a growing number of applications are dependent on the inferring knowledge from imagery. To clarify how deep architectures can achieve semantic segmentation, it should be revealed that “semantic segmentation is a natural step in the progression from coarse to fine interface [23]”. The first stage is to predict the whole image, referred to as classification. The second stage is to extract not only the class information but also the spatial information of the classes, related to as localization/detection. The last stage is to make the label predictions for each pixel to achieve fine-grained inference. A general framework for semantic segmentation can be considered as a hybridized model of decoder-encoder networks. The encoder represents a pre-trained classification network which is followed by decoder networks. The decoder transforms the discriminative features obtained by the encoder onto the pixel space to obtain a dense classification. Unlike classification tasks where the final result of

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the deep architecture is the only important issue, semantic segmentation maps the discriminative features obtained by the encoder onto the pixel space as well as the requirement of discrimination at a pixel level. Some representative deep architectures are region-based semantic segmentation (R-CNN) [24], fully convolutional networks (FCN) [25], and weakly supervised semantic segmentation [26].

2.2

Artificial Bee Colony

A variety of social swarms like ants, fishes, and birds collectively behave in nature to achieve a particular purpose. Moreover, they do not require any centralized management or supervision to perform complex and intelligent behaviors. To be considered as a type of intelligent swarm, a swarm should have the following characteristics of self-organization: • Positive feedback: An intelligent swarm tends to carry out beneficial activities that provide positive impact on their particular purpose. • Negative feedback: An intelligent swarm tends to avoid aversive situations that adversely affect the overall goal of the swarm. • Fluctuations: An intelligent swarm is able to perform random activities for creativity. • Multiple interactions: The members of an intelligent swarm exchange information with each other for the information transmission over the entire framework. The foraging behaviors of honey bees can also be considered in the concept of swarm intelligence. The foraging behaviors mainly depend on the following three elements: the food sources, the employed foragers (bees), and the unemployed foragers. There are two main parameters affecting the behaviors of honey bees: the election of food sources and the termination of food sources with low depositories. An employed forager is responsible of a food source which is currently under evaluation by him. The employed foragers carry information concerning the food source (e.g., direction, distance, richness) into the hive. After that, the employed foragers share the information with onlooker members through waggle dance. Unemployed foragers are responsible of searching for new food sources. While the scouts are new candidates for exploration, the onlookers are waiting around the surrounding of the nest (dancing area) for food search based on the information shared by employed foragers. The profitability of food sources is the main motivation of onlookers to direct their exploration. One of the representative samples modeling the foraging behaviors of honey bees is the artificial bee colony (ABC) algorithm [11, 27]. In ABC model, a source represents the solution for the problem, and its nectar amount represents the quality (fitness) of the corresponding solution. ABC comprises only three parameters: the population size, the maximum number of cycles, and the limit parameter. The limit

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parameter controls whether there exists any exhausted source. The general architecture of ABC algorithm is presented in Algorithm 1. Algorithm 1 Fundamental steps of ABC algorithm Initialization: 1: Set the control parameter values 2: Initialize the positions of food sources 3: Calculate the nectar amounts of food sources 4: repeat Employed Bee Phase: 5: Each employed bee searches in the neighborhood of his food source 6: Calculate the nectar amount of the new source 7: Greedy selection between the current and new sources Onlooker Bee Phase: 8: For each onlooker bee, select a food source in a probabilistic manner 9: Each onlooker bee searches in the neighborhood of his food source 10: Calculate the nectar amount of the new source 11: Greedy selection between the current and new sources Scout Bee Phase: 12: Memorize the position of the best food source 13: Check whether there exists a exhausted source 14: A scout bee replaces an exhausted source with a new source 15: until Termination criterion is met

In the initialization stage, the values of control parameters are predefined, and a predefined number of food sources are randomly initialized by Eq. (7) within the predefined boundaries.   ub lb xij ¼ xlb j þ randð0; 1Þ xj  xj

ð7Þ

where i 2 {1, …, CS} and j 2 {1, …, D}, CS is the number of food sources and D is lb the dimensionality of the problem; xub j and xj are the upper and lower bounds of the jth parameter; rand(0,1) is the randomly generated number between 0 and 1; xij represents the jth parameter of the ith food source. In the employed bee stage, each employed bee finds a new source in the neighborhood of the corresponding food source by Eq. (8). Once a new source is found, the greedy selection is applied between the new source and its parent. If the new source is better one, it is retrained for the population. Otherwise, the counter of the current (parent) source representing the number of unsuccessful trials is incremented by one.   0 xij ¼ xij þ /ij xij  xkj

ð8Þ

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where /ij is a uniformly generated number within the range of −1 and 1; k
{1, …, CS} represents the index of a randomly selected food source s.t. k 6¼ i; j is a randomly chosen dimension. After the employed bees complete their search, they back to the hive and share the information concerning the sources with the onlooker bees waiting in the hive. The onlooker bees select food sources in a probabilistic manner using Eq. (9). It is inferred from Eq. (9) that the onlookers tend to select high profitable sources but do not always choose the fitter sources. By this way, a balance is kept between exploration and exploitation processes. After the probabilistic selection process of food sources, the onlooker bees try to find new sources using Eq. (8). Then, greedy selection is carried out between the current and new solutions to determine the surviving source as in the employed bees’ phase. If any current source is still represented in the population, its counter representing the numbers of unsuccessful trials is increment by one. fitnessi pi ¼ PCS j¼1 fitnessj

ð9Þ

where fitnessi represents the nectar amount of the ith food source. After the onlooker bees complete their search, the counters of the sources are compared with the specified control parameter, referred to as limit. If there exists any source which exceeds the limit value at most, this source is determined as exhausted and its corresponding employed bee becomes a scout bee. In each cycle of ABC algorithm, the role of one employed bee becomes a scout and tries to find a new source by Eq. (7). For the determination of the limit parameter, there are no strictly defined cases. For instance, it can be defined as ∞ which excludes the scout bees’ phase from the ABC model or a value can be determined by considering the number of sources and the dimensionality of the problem as follows. limit ¼ a  CS  D; a 2 Q þ

ð10Þ

3 Applications of ABC for Image Segmentation 3.1

Thresholding

A variety of methods have been developed in the literature for image thresholding. However, most of them are computationally intensive and may not work properly in complex image sets. Moreover, the formulations of some representative thresholding methods (e.g., Kapur [18], Otsu [19] and Tsallis [28]) are not efficient for multilevel thresholding. To cover such issues, ABC algorithm like any other well-known evolutionary computation technique has received increasing attention of researchers to find optimal thresholds in an image.

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Horng [29] designed a new ABC-based multilevel thresholding method based on the maximum entropy criterion inspired by the Kapur’s entropy. The proposed method outperformed some popular PSO variants in terms of both the segmentation performance and the computational efficiency. Kumar et al. [30] introduced a 2D multilevel thresholding method using ABC and Otsu. In this method, /ij (F) in Eq. (8) is determined as follows. ( F ð G þ 1Þ ¼ F ð G þ 1Þ ¼

Fl þ rand1

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rand22 þ rand23

Fu ; otherwise 2  Fl  FG þ 1 ; if FG þ 1 \FL 2  Fu  FG þ 1 ; if

ð11Þ

FG þ 1 [ FL

Akay [31] introduced a comparative study of ABC and PSO algorithms to find optimal thresholds in an image. In this study, the between-class variance and the Kapur’s entropy were individually reformulated as the objective functions and were adopted into the ABC and PSO frameworks. According to a number of experiments, ABC can obtain more effective segmentation results than PSO. Charansiriphaisan et al. [32] introduced a comparative study of several ABC and PSO variants for multilevel image thresholding. It can be inferred from results that the best/one search strategy significantly increased the performance of ABC in multilevel thresholding tasks, defined as follows.   x0ij ¼ xbest;j þ /ij xr1j  xt2j

ð12Þ

where r1 and r2 are randomly chosen sources in the population. Zhang and Wu [33] designed an ABC-based thresholding method using the Tsallis entropy due to its superior performance against traditional thresholding criteria such as between-class variance and maximum entropy. The results showed that ABC could achieve more optimal thresholds by using the Tsallis entropy than by other criteria, and ABC could complete the evolutionary process in a shorter time than GA and PSO. Ye et al. [34] introduced a method using 2D Fisher criterion and ABC. In this method, the spatial information of a pixel was considered as well as its intensity value. According to the results, 2D Fisher criterion achieved better results than 1D Fisher and 1D Otsu. Ouadfel and Meshoul [35] introduced a comparative study of ABC, PSO, and DE algorithms for bi-level thresholding. According to the results, ABC and DE produced similar results in terms of uniformity and PSNR criteria. Bhandari et al. [36] introduced improved ABC-based thresholding methods using the Kapur, Otsu, and Tsallis criteria for the segmentation of remote sensing images which utilized chaotic routines and the opposition-leaning principles to produce fitter initial food sources. Besides the improved initialization scheme, the improved ABC-based thresholding methods adopted the search strategy of the pure differential evolution algorithm to find fitter food sources. The results showed that the enhanced methods could find optimal threshold values compared to the standard

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ABC and PSO algorithms. Moreover, the modified ABC algorithm achieved the best segmentation results over the Kapur entropy among thresholding criteria. Cueva et al. [37] proposed an ABC-based multilevel thresholding method, which computed threshold values using the probability error of two adjacent Gaussian functions. Compared to the expectation-maximization and Levenberg-Marquardt methods which were applied to determine Gaussian mixtures, the proposed ABC-based method can enhance the segmentation performance. In another work [38], the quick ABC algorithm was redesigned using different distance strategies in the onlooker bee phase for multilevel thresholding. The results revealed that the proposed quick ABC method could perform better than some representative evolutionary techniques.

3.2

Edge-Based Segmentation

Edge detection is a fundamental tool in pattern recognition and computer vision. The main idea is to extract edges from an image by identifying sharp changes in pixels. A variety of filters such as Laplacian, Sobel, and Canny have been successfully applied to edge detection. However, they are computationally intensive due to the requirement of an operation set for each pixel in the image. Therefore, there is a need to develop efficient methods to detect edges in an image using different techniques. ABC has also been designed for edge detection to address this issue. Cuevas et al. [39] introduced an automatic ABC-based method to detect multiple circular shapes from complicated images. Each solution in the population was represented as a three dimensional edge point set which built a circle such that f ðx; yÞ ¼ x2 þ y2  r 2 . To measure the difference between the pixels in a solution and the pixels in the edge map, the following objective function was used: PNs J ðC Þ ¼ 1  1 E ðsv Þ ¼ 0;

E ðsv Þ Ns if ðxv ; yv Þ; is an edge point otherwise v¼1

ð13Þ

where C is a circle candidate, and Ns is the number of pixels corresponding to C. Banharnsakun [40] proposed a two-stage segmentation methodology using ABC. In the first stage, an optimal edge filter was obtained by minimizing the difference between the training image and its corresponding edge map through Eq. (14). The optimal filter was then applied to the input image. In the second stage, ABC-based thresholding method was applied to the enhanced image obtained from the previous stage to obtain optimal threshold values.

Artificial Bee Colony: Theory, Literature Review, …

DðW Þ ¼

n X x ¼ 0; ER ðnÞ ¼ Ew ðnÞ xi i otherwise xi ¼ 1; i¼1

59

ð14Þ

where ER(n) and Ew(n) represents the training image and its corresponding edge map, respectively. Other ABC-based studies in edge detection can be found in [41, 42].

3.3

Clustering-Based Segmentation

Traditional clustering algorithms have been successfully applied to image segmentation. However, traditional clustering algorithms have the following drawbacks. First, they are very dependent on initial conditions. For instance, when the clusters are not initialized concerning the characteristics of the data, the performance of a clustering algorithm will not be adequate. Second, they tend to converge local optima, i.e., they are not able to deeply search the possible solution space. To address such drawbacks, evolutionary computation techniques such as ABC, PSO, and GA are strongly suggested to be a good option for clustering tasks [20]. Hancer et al. [43, 44] proposed an image clustering method using ABC and then applied this method to brain tumor segmentation. The employed objective function is defined Eq. (15). fit ¼ w1 dmax ðZ; xi Þ þ w2 ðZmax  dmin ðZ; xi ÞÞ þ w3 Je

ð15Þ

where w1, w2, and w3 are the coefficients keeping balance between the components, dmax represents the intra-cluster distance, dmin represents the inter-cluster separation, Je is the quantization error, Z represents the input image, Zmax represents the maximum intensity value within the input image, and xi = {mi1, mi2, …, miK} represents the K number of cluster centroids. Ozturk et al. [45] introduced an improved version of Eq. (15) as an objective function for ABC-based image clustering, defined by Eq. (16). This objective function does not require user-specified weighting parameters. According to a number of experiments on several benchmarks, it can be revealed that the improved objective function achieved the best segmentation performance in terms of internal validation, when it was integrated into the ABC framework. pfit ¼ Je 

dmax ðZ; xi Þ  ðdmax ðZ; xi Þ þ Zmax  dmin ðZ; xi Þ þ MSEÞ dmin ðZ; xi Þ

ð16Þ

Ozturk et al. [46, 47] developed automatic ABC-based clustering methods for image segmentation which did not require the information concerning the number of clusters. To develop such methods, a genetically inspired binary ABC variant

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and a similarity scheme-based ABC variant were proposed. To evaluate the quality of possible solutions, the VI index [48] was employed: VI ¼ ðc  N ðl; rÞ þ 1Þ 

intra inter

where c is a user-specified constant, N(l, r) represents the Gaussian distribution, intra represents the intra-cluster distance, and inter represents the inter-cluster separation. Ozturk et al. [49] introduced an hybridized ABC-based color quantization method which utilized K-means in a probabilistic manner for few iterations to get rid of the adverse effects of initial conditions in the population. To evaluate the solutions in the population, the mean square error (MSE) was considered as an objective function, defined by Eq. (18). The results indicated that the ABC-based quantization method outperformed well-known clustering algorithms such as Kmeans and FCM. MSErgb ¼

K X  2  2  2 1 X Rp  Rk þ Gp  Gk þ Bp  Bk Np k¼1 8z 2 C p

ð18Þ

k

where zp is the RGB pixel of the image with the intensity (Rp Gp Bp), Np is the total number of pixels in the image, (Rk Gk Bk) is the kth cluster centroid in a solution, and K is the total number of centroids. Balasubramani and Marcus [50] proposed a hybridized segmentation methodology using ABC and FCM for brain MRI segmentation. Alrosan et al. [51] introduced another hybridized segmentation methodology using ABC and FCM for brain MRI segmentation. In this methodology, ABC was applied to search in the possible solution space, and FCM was applied to evaluate the possible solutions. Another methodology [52] which followed similar procedure as in [50] was used to segment retinal blood vessel. According to the results, the developed methodology obtained succesfull segmentation performance compared to several applications in the literature. More information concerning the applications of ABC for clustering can be found in [17, 53].

3.4

Learning-Based Segmentation

In recent years, ABC has been applied to improve the performance of artificial neural networks (ANN) architectures. Karaboga and Akay [27] tried to find an optimal set of weight parameters by minimizing the total error through ABC. Karaboga and Kaya [54] optimized antecedent and conclusion parameters of adaptive-network-based fuzzy interference system (ANFIS) by minimizing the average mean square error through ABC. Bullinaria and AlYahya [55] optimized the connection weights of feed-forward networks to improve the performance of

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backpropagation. Due to local minima problems of traditional training architectures, Ozturk and Karaboga [56] introduced a hybridized model of ABC and Levenberg-Marquardt algorithms for optimizing the parameters of a training network. The success of ABC in training ANN architectures has attracted researchers’ attention to train the architectures of deep neural networks. Badem et al. [57] combined ABC with the L-BFGS algorithm to train deep neural networks. Banharnsakun [40] integrated a distributed ABC algorithm into the CNN framework to optimize the initial weights of CNN. However, it is not possible to find the applications of ABC with CNN in semantic image segmentation. In other words, this issue has recently come into consideration.

4 Implementation of ABC to Brain Tumor Segmentation Brain tumor segmentation is one of the most difficult tasks in medical imaging due to the motion of patients, soft tissue boundaries and limited acquisition time. Brain tumors may be present in a variety of shapes, sizes, and image intensities. Furthermore, according to the World Health Organization (WHO), nearly 400,000 people are diagnosed with a brain tumor in the United States every year, and most of such tumors involve cancer structures, unfortunately. Before carrying out the treatment of a brain tumor, it is necessary to determine the locations, boundaries, and regions of the tumor. As magnetic resonance imaging (MRI) can produce more detailed images than other techniques, MRI-based techniques are employed to detect the brain tumor according to their anatomy and physiology. Traditionally, the segmentation process of brain MRI tumors is manually done by radiologists. Manual segmentation is, however, an intensive process and may lead to unavoidable mistakes.

4.1

ABC-Based Segmentation Methodology

In this section, we introduce an ABC-based segmentation methodology to extract tumors from MRI images. The flowchart of the ABC-based segmentation methodology is presented in Fig. 3. The methodology has three key stages:

Fig. 3 ABC-based segmentation methodology

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(1) Preprocessing: It is no doubt that a high-quality image involves unwanted and noisy information as well as necessary information. The unsolicited and noisy information in the image adversely affects the segmentation process. Therefore, it is necessary to eliminate the unwanted information in the image using some well-known filters such median, average, Gaussian, etc. For this process, we choose 2D 3  3 median filter that can properly work in the noisy platform, especially in eliminating salt-pepper noise. The median filter changes the intensity value of each pixel with the median of the intensity values within the local neighborhood. (2) Processing: After the elimination process of noisy information, the ABC-based clustering method [44] is applied to segment input images into regions. The general schedule of the method is as follows. • Initialization: Initialize each food source in the population with K pre-specified number of cluster centroids. For each solution, assign each pixel in the image to the closest centroid using Euclidean distance and then evaluate the fitness value by Eq. (15). • Employed Bee Phase: Find a new source in the neighborhood of each food source using Eq. (8) and then evaluate its fitness value by Eq. (15). If the fitness value of the new source is fitter than that of the current one, the new source is kept in the memory instead of the current one. • Onlooker Bee Phase: Calculate probabilities of each food source by Eq. (9) and then choose a food source depending on the probabilistic information. Then, search for a new source in the neighborhood of the selected food source using Eq. (8) and evaluate the fitness value of the new source by Eq. (15). Finally, apply greedy selection between the current and new sources to determine which source is kept in the memory as in the employed bee phase. • Scout Bee Phase: If there exists any source which is kept in memory for a predefined number of trials (called limit), it is changed with a new source. • Output: Select the best source and then segment the input MRI image using the selected centroid set. (3) Post-processing: After the segmentation process of the input MRI image, the tumor extraction process is carried out. First, image filtering is applied if there exist a large number of noisy pixels. Then, the gray-level thresholding is applied to get a binary image. Finally, connected component labeling based on pixel connectivity is performed.

4.2

Experimental Results

The effectiveness of the ABC-based segmentation methodology is verified by comparing it with K-means, FCM, and GA algorithms on nine brain MRI images

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with the size 256  256 in terms of the VI index [48] and the CS measurement [58]. The parameter values for ABC and GA are determined as follows. The population size and the maximum number of cycles are chosen as 50 and 250, respectively. The limit parameter in ABC is set to 100. The crossover and mutation rates in GA are, respectively, set to 0.8 and 0.2. The l fuzziness parameter in FCM is set 2. The number of clusters (K) is chosen as 3 for the first three MRI images and 4 for the remaining images. The results of the clustering algorithms are presented in Table 1 over 30 independent runs in terms of mean and standard values. According to Table 1, segmentation methods based on evolutionary techniques can achieve better clustering quality than traditional clustering algorithms without no doubt. In other words, evolutionary computation-based techniques can obtain well-separated and homogeny clusters compared to traditional algorithms. We provide Fig. 4 to clearly illustrate the difference in terms of clustering quality. As seen in Fig. 4, ABC-based methodology can correctly extract tumors from MRI images. However, K-means and FCM cannot be treated as adequate in the segmentation process. When making comparisons between evolutionary techniques, it can be inferred that ABC outperforms GA in terms of the VI index in all cases and is superior to GA in terms of the CS measurement except for some cases. In summary, the ABC-based segmentation methodology can provide better segmentation quality and thereby well detect the boundaries and regions of tumors in MRI images.

Table 1 Results of criteria Dataset MRI70 MRI80 MRI90 MRI100 MRI110 MRI120 MRI130 MRI140 MRI150

ABC CS

VI

GA CS

VI

FCM CS

VI

K-means CS VI

0.61 (0.01) 0.58 (0) 0.51 (0) 0.60 (0.02) 0.70 (0.01) 0.64 (0.01) 0.56 (0) 0.67 (0) 0.53 (0.02)

0.16 (0) 0.16 (0) 0.16 (0) 0.08 (0.03) 0.08 (0.01) 0.07 (0) 0.10 (0) 0.07 (0) 0.09 (0.01)

0.62 (0.06) 0.60 (0.05) 0.52 (0.02) 0.59 (0.04) 0.68 (0.05) 0.59 (0.04) 0.61 (0.06) 0.59 (0.05) 0.56 (0.03)

0.18 (0.02) 0.17 (0.02) 0.17 (0.02) 0.10 (0.02) 0.11 (0.03) 0.10 (0.02) 0.11 (0.02) 0.11 (0.03) 0.11 (0.03)

0.81 (0) 0.81 (0) 0.67 (0) 0.91 (0) 0.89 (0) 0.69 (0) 0.76 (0) 0.74 (0) 0.69 (0)

0.22 (0) 0.20 (0) 0.19 (0) 0.13 (0) 0.10 (0) 0.11 (0) 0.11 (0) 0.10 (0) 0.12 (0)

0.81 (0.01) 0.83 (0.01) 0.70 (0) 0.97 (0.03) 1.02 (0.03) 0.71 (0.02) 0.80 (0.01) 0.77 (0) 0.73 (0.01)

0.22 (0) 0.21 (0) 0.20 (0) 0.12 (0.01) 0.13 (0) 0.12 (0) 0.14 (0.01) 0.11 (0) 0.13 (0)

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Fig. 4 First column represents input MRI images and the remaining columns are, respectively, the results of ABC, K-means, and FCM for each MRI image

5 Conclusions The overall goal of this chapter is to reflect the profile of ABC in image segmentation. To achieve this aim, we first provide a theoretical background on ABC and image segmentation and then briefly survey the applications of ABC in this field. Considering ABC-based applications proposed for image segmentation, it is observed that there exist a large number of studies in the literature from the perspective of thresholding and clustering. The general idea of many ABC-based thresholding methods is to optimize the most widely used thresholding criteria (e.g., Kapur, Otsu, etc.) through ABC variants. However, current thresholding criteria may not work properly especially in noisy image sets. There is a need to develop new thresholding criteria to improve segmentation quality. The general idea of ABC-based clustering methods is to optimize internal indexes (e.g., mean square error, VI index, etc.) through ABC variants. Compared to thresholding, there exist lots of criteria proposed for clustering. However, there exists no such a comprehensive analysis in the literature that shows which clustering criterion is more suitable for ABC and its variants to achieve high segmentation performance. Compared to thresholding and clustering, the number of ABC-based applications from the perspective of edge-based and learning-based is very few. Although ABC and its variants have integrated into ANN architectures to increase the performance of ANN dramatically, it is not possible to find an application of ABC in the literature that tries to improve the performance CNN and its variants for semantic

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image segmentation. In our opinion, these open issues will become favorite research topics for the future works of researchers. Finally, we present an ABC-based segmentation methodology that extracts brain tumors from MRI images to prove the effectiveness of ABC in this field. It can be inferred from the results that the ABC-based segmentation methodology can find an optimal centroid set that yielding higher segmentation performance and thereby achieving well-extracted regions.

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Applications of Memetic Algorithms in Image Processing Using Deep Learning K. Laby, M. Sudhakar, M. Janaki Meena and S. P. Syed Ibrahim

Abstract Estimating the cost based on the damage for a given vehicle for insurance processing is a significant area and having huge scope for automation. At the point when a car gets harmed in a mishap, the insurer of the corresponding vehicle needs to take care of the expense. Human intervention in this process is costly and takes more time for visual inspection. By utilizing the advanced deep learning procedures, it is quite easy to prepare a model that will recognize the damaged car parts and estimate the cost accordingly. Convolutional neural network (CNN) is one kind of artificial intelligence technique commonly used for pattern recognition and classification. To get an accurate result, the model should be capable of anticipating what sort of maintenance precisely to follow. The model will be prepared by training the variety of images collected with different viewpoints of the same damaged vehicle before making the final predictions. We used transfer learning-based pre-trained VGG16 network architecture for this purpose. Though these algorithms are giving accurate predictions, memetic algorithms can still optimize the results. In addition to this, two other applications for detecting the car existence in a given image using deep learning useful for automated self-driving applications are presented.







Keywords Memetic algorithms CNN Object detection Car damage detection Semantic segmentation





Pre-trained model


K. Laby
M. Sudhakar
M. Janaki Meena (&)
S. P. Syed Ibrahim SCSE, VIT University, Chennai Campus, Chennai, India e-mail: [email protected] K. Laby e-mail: [email protected] M. Sudhakar e-mail: [email protected] S. P. Syed Ibrahim e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_4

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1 Introduction Nowadays, in the vehicle insurance industries, huge money is getting wasted due to exposures of claims. Leakage of claims or approval of claims is determined as the contrast between the actual claim that is paid and the money paid if overall major practices of the industry are applied. The traditional mechanism applied is a manual visual inspection and validation is used to optimize such issues. Nonetheless, they precede delays during the processing of claims. Some of the enterprises have made their efforts in order to reduce the processing time of claims [1]. Instant automated models for car insurance claims are high in demand in recent years. The computing machines with high power and the image-based datasets helped in the enhancement of computer vision to a greater extent in the past few years. In this chapter, we investigated and applied the existing state-of-the-art models to forecast a car in an image is damaged or not. For such situations, we used CNN-related approaches to classify the car-blurred kinds. The progress of deep learning in regarding computer vision industry set up a trend in AI. Considering image detection, the aim of the models is to extract the features logically and transform those into low dimensions and images with low noisy content and besides having actual features of the image used for classification. Supervised learning paradigms such as random forest, support vector machine (SVM), and the rest of the supervised learning paradigms are capable of solving the problems raised during classification otherwise adopted for training the classifiers [2, 3]. The automated applications are to be advanced so that to provide assistance in the insurance processing by using some of the deep learning approaches such as CNN and machine learning (ML) algorithms. The outcomes of these applications save money and time as well. Because of high-end graphical essentials, CNN model consumes more time which in return needs CUDA enabled GPU to do parallel processing and was found as time-consuming one. It takes much time to extract an image with noisy data [4]. The rate of accuracy is less for CNN under low-light situations. During image set classification, there raised the errors. The ML paradigm needs a system with the best specifications. The contrast between the earlier mentioned and laptop with minimum specifications is that it consumes more time for training. A neural network needs time for training and it relies on better specification. As neural network includes various matrix multiplication, so instead of using GPU, we used CPU since it comprises of many simpler cores when compared in CPU where there will be few simpler cores [5]. Memetic algorithms (MAs) are population-related meta-heuristic search techniques have received high attention in earlier years. A meta-heuristic is a type of heuristic algorithm that focuses on better outcomes and increasing over them (intensification) and to stimulate the analysis of solution space by expanding the scope of search into novel domains (diversification) can search for solution space very effectively. Various heuristic algorithms are much particular and problem-dependent. There prevail many benefits with memetic paradigms for issues in the image processing, data clustering, and graph coloring. In this chapter, we

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concentrated on how we can apply memetic algorithms over deep networks of the image processing-related applications to obtain optimized results [6]. The complete organization of this chapter is given below: (1) In Sect. 2, we presented the applications of MA. (2) In Sect. 3, the basic building blocks of CNN are presented. (3) In Sects. 4, 5, and 6, we considered the three deep learning-based image processing applications and how can one apply MA to these applications. – First, Automated Object Detection using CNN is described. Though this application detects the objects of various classes, we concentrate that the system is detecting the car or not. – Second, Automation of Self-driving Cars is described. For this, we applied semantic segmentation using a deep learning architecture. – Third, Automatic car damage detection is described. This is the main goal of this chapter. Here, the application is used to check whether the input image contains a car, if so whether the damage is present or not along with location and severity level. This kind of application is essential in car insurance industries. The details of these methods are explained clearly in the subsequent sections. (4) In Sect. 7, conclusions and future works are presented.

2 Applications of Memetic Algorithms MAs have various applications, mainly used for optimization problems, scheduling problems, robotics, and machine learning applications. In recent years, it also proved that MAs can be efficiently utilized for the optimization problems in image processing and computer vision. Here, we presented a brief description of the usage of MAs in each application.

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Optimization Problems

Conventional NP optimization problems represent the highly standardized field of MAs. The outstanding record of eminence was reported in the context of MAs applications to the NP-hard problems as follows: graph partitioning, bin-packing, quadratic programming, min number partitioning, set partitioning and particularly on the min travelling salesman problem and its variants, graph coloring, max independent set and quadratic assignment.

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Scheduling Problems

Indeed, the scheduling problems are the major optimization fields because of their practical implementations. MAs are used to work on huge kinds of scheduling problems like maintenance scheduling, total tardiness single machine scheduling, single machine scheduling with setup times and due-dates, open shop scheduling, parallel machine scheduling, flow shop scheduling, project scheduling, and so on.

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Machine Learning and Robotics

Machine learning and also Robotics are two domains which are approximately associated due to the involvement of multi activities while controlling the robots are usually approached by using artificial neural networks and/or classifier systems. In general, MAs are represented like ‘genetic hybrids’ and are used in both the domains such as in general optimization problems dependent to machine learning (consider the training of artificial neural networks) and also in the applications of Robotics. Considering the former, MAs are being applied over train the neural networks, recognition of patterns, classification of patterns and time-series analysis, and so on. Besides the applications stated in the above, MAs also used in the other domains like medical fields, economic, imaging science, oceanography, and so on. For more information regarding MA applications, we recommend querying the bibliographical related databases otherwise web browsers by hitting the keywords like ‘memetic algorithms’ and ‘hybrid genetic algorithm.’

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MAs in Image Processing

In recent times, tremendous images of related databases are available globally. So to use those databases, we require approach retrieval and search approach which is effective and also robust. The conventional procedure to retrieve an image is done by representing each and every image with some text-based annotation and the image are retrieved by searching with the help of some keywords. This procedure is complex and unclear due to its fast increment in images count and varieties of image-related contents. By means of this, the Content-Based Image Retrieval (CBIR) attains big attention. Few of the researches in the past decades were striving for image retrieval from the large databases through analyzing the contents of images since CBIR was an active domain in the 1990s specifically for research on the multimedia community. An efficient CBIR model is generated by the application of MA for retrieving the images from the databases. When a user gives a query image as an input, the introduced CBIR model extracts the features of that image like color, signature, shape, and so on. Utilizing

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the MA-related similarity measure, images are relevant to QI are efficiently retrieved [7]. Multi-label Classification is one of the emerged challenging issues in various fields of applications like text classification, classification of gene function, and semantic definition of images. The purpose of the multi-label classification is allocating hidden patterns to different categories has developed in recent applications. Genetic-algorithm based multi-label feature-selection approach is considered as purposeful approach due to its success in improving the preciseness of multi-label classification. After all the genetic algorithms are restricted to recognize the fine-tuned featured subsets which are approximate to the global optimum. There are various memetic feature-selection paradigms for the multi-label classification which ignores the incomplete convergence and increases the efficiency [8]. Synthetic Aperture Radar (SAR) images accommodate purposeful data to various applications because of its acquisition of complete weather, huge coverage, less replication, and high-resolution capacity. Nonetheless, the SAR images are native gets affected by the most implicative noise which is otherwise called as ‘speckle.’ This speckle develops in all kinds of imagery obtained from the consistent imaging systems like a laser, acoustic, and SAR imageries. Besides, few of the SAR images usually exhibit with low contrast. Speckle and minimum contrast result in an image with the degradation that minimizes the chances of target detectability and freezes the further exploration of the SAR images. It became a considerable issue in the image processing domain which increases the wanted target during the suppression of speckle noise of the SAR images. The researchers in [9] proposed an instant enhancement approach for the SAR images on the basis of Non Subsampled Contourlet Transform (NSCT) and memetic algorithm (MA). Tomography is considered as one of the major domains of image processing. Practically, it is used in many fields, when the data related to the internal structure of an object are required without cracking it. In the few optimization issues, the work is to predict a set of data from some connected data that is known. The tomography will cope with the images reconstruction from their projections. MAs are examined over the triangular grids to reconstruct the binary images by using their three and six direction projections. The paradigm creates an initial population by utilizing the network flow paradigm for both of the input projections. The image quality is enhanced by switching the components and also compactness operator [10].

3 Building Blocks of CNN In the previous section, we have seen the applications of MAs in different fields. In this chapter, we highlighted three different types of deep learning-based real-time applications and can be optimized using MAs. These applications use CNNs to train a model and for the classification process. Hence, we introduced brief details about the basic building blocks of CNNs in this section, to easily understand the

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convolution process in the subsequent sections. CNNs are a special category of neural networks and used mainly for image recognition and classification problems. These networks are successful in identifying the faces, objects present in the images, segmentation and autonomous operating vehicles, i.e., self-driving cars. In simple terms, CNNs are simply many layers of convolution operations with non-linear activation functions. Here, the non-linear activation functions are applied to the output of each convolution layer. In feedforward neural networks, each neuron present in one layer is connected to all the neurons present in the next layer, but in CNNs the fully connected layer will be present at the last stages.

3.1

Inputs and Outputs

At the point when the computer takes image as an input, it will see a variety of pixel values inside it. The number of pixel values is identified based on the resolution and size of the input image, for example, 64  64  3 has totally 12,288 pixels. Here, each pixel is a tuple of three values, i.e., height, width, and depth. Each value in this tuple denotes the intensity and is ranged in between [0, 255]. At the image classification process, these numbers are only the input accessed by the system, while the same numbers are good for nothing to us. The idea is that give these array of numbers to the system and get the output of the numbers that depict the likelihood of the object present in an image is being a specific class. (e.g., 0.90 for car, 0.5 for airplane, 0.5 for motorbike, etc.)

3.2

Structure of CNN

Technically, CNNs accept an image, pass it through series of convolutions, activation functions, pooling layers, and fully connected layers, and finally get the desired output. As discussed, earlier, the input for this procedure is an image, and the output might be a solitary class or likelihood percentage or value of being an object to a specific class. There are mainly four basic building blocks for every ConvNet and will be discussed in detail in this section. On the one hand, any color image captured from a digital camera will have ‘3’ channels, i.e., red, green, and blue. One can imagine that, three 2-dimensional arrays are stacked over each other as shown in Fig. 1. We considered 6  6  3 image for better representation, but in real time the images having more width and height with a depth of ‘3’ channels. On the other hand, a gray-scale image contains only one channel and only a single 2-dimensional array represents an image.

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Fig. 1 Stack of RGB array

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3.3

Convolution Layer (CONV)

CONV layer is the major building block in CNN and is almost similar to the normal convolution operation in image processing. The basic role of CONV layer is to extract the useful features from the image. Consider a small binary image of 5  5 size and a filter of 3  3 size, the convolution result of these two images is shown in Fig. 2. The green-colored matrix (Fig. 2a) denotes the binary input image, red colored matrix (Fig. 2b) is the filter or kernel image and the blue colored matrix (Fig. 2c) is the output image. In this case, the colors are only for representation and do not have any specific meaning. We slide the filter image over the original image with a stride of one pixel. The convolution operation result is the sum of element-wise multiplication of two matrices. Here, the filter image can see only a part of the image in

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every stride. But, what about the case for a color image, which contains three channels. Consider an image with size of 32  32  3, and the filter size is of 5  5  4. The number of channels in the input image and the filter is same. When the filter is convolved with the input image, we will get an image with 28  28  1 as shown in Fig. 3. The outcome images are dependent on the number of independent filters. If the number of filters is ‘6,’ then we get 6 feature maps of size 28  28  1 as shown in Fig. 4. There will be a number of convolution layers in sequence.

3.4

Pooling Layer

Pooling layers are used to minimize the dimensionality of the image at each stage, when the image becomes too large by retaining the useful information. Pooling may be of three types such as max pooling, average pooling, and sum. For example, if the feature map is 4  4 matrix, the max pooling will be derived as shown in Fig. 5. For the stride value 2, we slide the window 2  2 at each time, find the maximum value in that region. From this, the dimensionality of the feature map will now become the size of 2  2 from the size of 4  4. Similar to this, the pooling layer will take an image as input with a specific dimension and with the help of max pooling, the image is reduced by half of its original size as shown in Fig. 6.

3.5

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As mentioned in the previous section, after each convolution process, a non-liner activation function is applied in CNN. ReLU is one of non-linear activation functions and the purpose of this unit is to replace all the negative pixel values with

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0. Since the convolution operation results negative values (linear), we used to apply any non-linear activation function to remove the negative values by replacing it with 0. In other words, we take the maximum value from 0. For a 4  4 matrix, the same is shown graphically in Fig. 7.

3.6

Fully Connected Layer (FC)

Like multilayer perceptron, FC is a softmax function used at the output layers. The name FC denotes that every neuron in a specific layer is connected to all the neurons present in its next layer. The result of convolution and pooling layers is the high-level features extracted from the image. The reason for the FC is to utilize these features to classify the input image into distinct classes based on the training set. For example, if the image classification task is to identify that the image contains a car or not, then the corresponding fully connected layer from the input layer and the output of the fully connected layer.

4 Object Detection Using CNN The fundamental concept of object detection is that recognize the name of the object present in the given image. Recent detection systems reuse classifiers to do detection. These systems consider a classifier for every object to detect and examine it at different locations in sample image. Systems such as deformable parts models (DPM) apply an approach known as sliding window strategy in which the classifiers are constantly run in an entire image [11]. Very recent techniques such as R-CNN uses region proposal approaches to initially create potential bounding

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boundaries in an image and later on run a classifier on those proposed boundaries. Later performing classification, the pre-processing is used for refining the bounding boundaries and removes the replicated detections [12]. We review object detection like single regression issue, straight from pixel of image to bounding boundary coordinates and probabilities of class. By using our system You Only Look Once (YOLO) at an image, we can guess the existing objects and their location in an image. It is simple to process the images using YOLO. Our system (1) resizes the input image into 448  448, (2) runs a single convolutional network over an image, and (3) thresholds the resulting detections based on model’s confidence. Simultaneously, a mono-convolutional network forecasts various bounding borders and its probabilities of class. YOLO, a unified design has many advantageous than conventional object detection models. This process is shown in Fig. 8. Simply, we run our neural network over an image at sample time to guess the detections. Our basic network works at obtaining 45 frames per moment without batch processing over Titan X GPU and speedy version works at obtaining 150 frames per moment. Moreover, YOLO achieves more than twice the mean average precision of other real-time systems [13, 14]. The idea here is the integration of the framework namely MobileNet and SSD yields good and speedy outputs considering deep learning-related object identification. MobileNet was trained with the help of COCO (Common Objects in the Context) dataset. This trained design comprises nearly 2000 classes of objects. We have taken different kinds of images as input and have applied SSD network model and obtained outcomes. Approximately, 15–20 objects can be detected and those

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Fig. 8 Basic object detection using CNN

Fig. 9 Object detection using CNN; a Input image, b object detection result

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may include cars, pedestrians, birds, etc. Some of the input images along with the object detection outputs are presented in Fig. 9. Four parameters are required for this method.

5 Semantic Segmentation Semantic segmentation is mainly useful to classify different object in real-time applications such as autonomous self-driving of cars. We are describing the same kind of classification with the help of another kind of deep learning model, i.e., E-Net architecture. The models of semantic segmentation are generally modeled and conferred in this chapter. Additionally, we also provided total state-of-art approaches. So it enables us to set up the remaining models. After all most of those have the similar basic requirements, settings, and flow. So it is easier to implement and also to do end-to-end training. Your choice of usage relies on your requirement with respect to accuracy or speed or memory. Figure 10 describes the representative segmentation pipeline of conventional architecture.

5.1

E-Net Architecture

The ENet model includes different phases and is represented by the horizontal lines and the first digit after each block. The output image size is 512  512. This model utilizes ResNet [15] which depicts individual key branch and also extensions including with the convolutional filters that divide from it and later it merges back with the element-wise addition and is shown in Fig. 11b. In this chapter, we used deep learning ENet models for performing the semantic segmentation. With those, we can apply on both the images and videos. The major Fig. 10 Pipeline of classification process Window Extraction

Preprocessing Feature Extraction Window wise Classification

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advantage with the ENet model is that it is 18 times faster, requires only few parameters, and yields better accurate rates when compared with the remaining models. The size of the model is only 4 Mb. It consumes 0.2 s in CPU for execution time at one pass. Rest of the models do the same task are ResNet, Alex Net, Google Net, and VGG-16. On the basis of some instances of classes such as road, buildings, vehicles, and so on, the model is trained. Some of the packages we used here are numpy, argparse, OpenCV, imutils in our work. The ENet model uses fewer parameters, requires less space, and it will be 1 Mb around. The major stages of semantic segmentation include classification, localization or detection, and finally semantic segmentation (Fig. 12b).

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Fig. 12 a Input Image, b segmented result

6 Automatic Car Damage Detection Few of the AI techniques enable the patterns recognition and image classification is the CNNs. These are modeled for classifying many categories by using filter packages in which the parameters got trained from huge databases related to various images for every category like Alexnet [16]. A vivid example related to transfer learning by using CNN was developed by [17]. CNNs employ on multiple application domains and also there are many studies which are intended to describe their work to apply the deconvolution approaches as stated in [18]. In [19] vehicles which captured with less resolution cameras are identified and also be classified and make it to reach 94.7% of accuracy rate. The network whichever trained over a source operation is utilized like feature extractor in target work. Various CNN models are trained over ImageNet are available as open-sources like VGG-16, VGG-19, Alexnet, Cars, and ResNet. Conventional machine learning strategies are analyzed for the instant damage assessment. Jaywardena et al. [20] suggested a way

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to detect vehicle damage by applying 3D CAD design of vehicle that is fit (undamaged). Through performance analysis of CNNs over image classification, we can decide the well-suited application. It is noticeable that complex frames will often lead to confusion while detecting the scenes. For instance, physical objects such as cot and chairs which can be identified easily but trained networks leads to confusion and hence varies in accuracy. Based on all these observations, practically it was proven that the trained networks together with transfer learning perform better than the prevailing ones and yields greater accuracy rates. Therefore, many more layers result in more training so that best accuracy rates are obtained.

6.1

Transfer Learning

Transfer learning is one kind of artificial intelligence technique in which a model is trained for one task and can be reused for another task. It is a kind of memetic technique for optimization that is useful for performance improvement and quick result for the second task. In the develop model approach, first a source task will be selected and a one can develop a model in a professional environment. Then, this pre-trained model will be reused for any other task. This model can be tuned or refined optionally based on the changes in problem statement or input. In the pre-trained model approach, there exist many pre-trained models developed by several research organizations or developed by the competitors from the community such as ILSVRC. This model can be utilized as a beginning point for the second task. Similar to the first strategy, this model can be tuned or refined optionally based on the changes in problem statement or input. Though there exist two strategies for this, the pre-trained method is commonly used in deep learning community. The fundamental benefits of the transfer learning are higher start, higher slope, and higher asymptote as shown in Fig. 13. The skill while developing the base model is high before refining, and improvement of the skill at training stage of the base model is steeper and converged skill of the base model is asymptote. We opted the model VGGNet by Simonyan and Zisserman et al. [21], from the top visual recognition challenge ILSVRC. This network architecture of this model is relatively simple and effective and described briefly in the next section.

6.2

VGG Network

The network architecture of the VGG model contains absolutely 16 layers; hence, this model spoken as VGG16. As mentioned in the earlier section, this model was trained for the ILSVRC 2014 challenge. Approximately, 90% of the parameters of

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Fig. 13 Performance graph of transfer learning

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this model originate from the last ‘3’ FC layers. The model is optimized and utilized as another variant called SmallerVGG net. SmallerVGG net utilized 3  3 convolutional layers and are stacked up each other in increasing depth. We can use max pooling to reduce the size of the image at the next levels. FC layers are used at the end of the layer before using softmax function. The model will take ‘4’ parameters as input: width, height, depth, or number of channels and the number of classes. For example, if we want to classify the input image contains a damage or not, the number of classes can be taken as 2 (i.e., damaged or not damaged). We will be working with images of size 96  96  3, if our input image is having different size, first it will be rescaled. We can change the input size to 224  224  3, 32  32  3, etc., as per the requirement. We took 32 convolution filters with size 3  3, in its first layer, and use ReLU as activation function followed by batch normalization. Further, with the help of pooling the size of the image will be reduced and will get the feature maps of size 32  32. We can also utilize the dropouts used to disconnect the nodes from one layer to the next layer. In the subsequent level, the filter size got increased to 64 from 32 and the max-pooling size is reduced to 2  2 from 3  3 followed by Dropout. Finally, we have a set of FC followed by non-linear activation function and softmax layer to get the classification result. This process is shown in Fig. 14. In this, we have shown only the first block of the network, similar to this there will be another 15 blocks to reach the FC layer. The major problem with VGG16 is that, training time is more, since more number of layers. In any case, we can settle this issue by retraining just a limited part with few parameters with the help of transfer learning described in the previous section. In this method, acquire the pre-trained VGG network model and drop the output layer. The rest of the network serves the feature vectors for our dataset. For each image in the training set, we compute the features vectors and will feed into the existing network. Subsequently, we can train a simple by using a simple softmax classifier. For instance, we can also drop multiple layers at the end or just use the trained model and fine-tune the parameters on the new input set and train the model

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Fig. 14 Fragment of VGG16 architecture

accordingly. The optimal strategy relies based on the input size and its similarity with the ImageNet dataset. If the dataset is too small, fine-tuning may give the risk of overfitting in the network. In this, our input data contains mainly three categories: images without cars (negative samples), images with undamaged cars (positive samples without damage), and images with damage (positive sample with damage). The sample images of each category are shown in Fig. 15. The number of sample per each category is shown in Tables 1 and 2. Among which, we took the training and testing splits as 80 and 20%. For the negative sample, such as images without cars, we used Caltech-256 dataset. We also manually filtered by removing few images which contain car. We used the best and largest car image dataset by Stanford. The dataset contains around 17,000 images of 196 different car models, and mainly created for fine-grained classification. According to best of our knowledge, we did not found the benchmark dataset for car images with damaged parts, we collected around 2000 images from the Google with different queries. Our main goal here is to develop an application, which defects the damage severity for a given input image, and the flow of this work is shown in Fig. 16. However, we split this work into three separate tasks. The first task is car recognition, implies that the given input image contains the car or not; secondly, whether the car contains damage or not; and thirdly, detect the location in which the damage is presented, i.e., front, rear, and side along with severity such as minor, moderate, and severe.

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Fig. 15 Sample images from the dataset. a negative samples, b positive sample with damage, c positive sample with no damage

Table 1 Images per each category for training and testing

Image category No cars Undamaged cars Damaged cars

Training 5402 5402 1842

Testing 1350 1350 462

Total 6572 6752 2304

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Front Rear Side Minor Moderate Severe

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Fig. 16 Flow of car damage detection

Now, we are set to apply the transfer learning using VGG16, since the dataset is moderately same as that of ImageNet. At first, we trained the model without fine-tuning at the CNN layer to avoid the overfitting on small dataset. FC also becomes very small, since we have at most three classes. We used the number of classes as two for recognizing the car or not.

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Table 3 Evaluation results without fine-tuning Task

Accuracy

Precision

Recall

HScore

Recognize cars For damaged For car parts For severity

0.86 0.78 0.56 0.61

0.81 0.8 0.47 0.61

0.83 0.79 0.47 0.55

0.81 0.79 0.38 0.5

Table 4 Evaluation results with fine-tuning Task

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0.96 0.86 0.74 0.73

0.95 0.9 0.71 0.65

0.9 0.76 0.7 0.64

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Fig. 17 a Result without fine-tuning, b with fine-tuning

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Fig. 18 Confusion matrix for a recognizing a car, b car damage, c severity without fine-tuning, severity with fine-tuning

Once again we trained the model, with the help of tuning. The result of these two cases is shown in Tables 3 and 4. The comparison graphs are shown in Fig. 17. The confusion matrix for car recognition, damage detection, and severity recognition without and with fine-tuning are shown in Fig. 18. With the help of transfer learning and VGG16, we got a decent accuracy for recognizing the damage in the car. Increasing the size of dataset and tweaking the hyper-parameters may give even more increase in the accuracy.

7 Conclusion The advancement of AI techniques for the classification problems has come address wide fields of use as they improve the outcomes of existing methods, permitting the improvement of applications in image processing and computer vision. We concentrated on these three applications in this chapter and described how the MAs can helpful for optimization for the deep learning techniques. First, we described the major applications of MAs in computer science from graph coloring to advanced deep learning methods. Further, we describe the major applications of image processing such as object detection, semantic segmentation, and damage detection in the vehicle industry. We used YOLO object detector for object detection, in which especially we concentrated that the application is detecting the car objects in the

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image or not. In addition, we described the semantic segmentation which is useful for self-driving cars to segment the road regions from the other objects with the help of E-Net architecture. Finally, we used transfer learning based on VGG16 Network model to classify the car damage detection in the given image. Further, applying the other pre-trained models for the same dataset and comparing the results of VGG16 will be considered as our future work. In addition, most of the deep learning mechanisms are giving accurate results based on the input parameters. In our next work, we also try to find the best hyper-parameters using MAs to improve the accuracy of the deep learning networks.

References 1. Patil, K., Kulkarni, M., Sriraman, A., Karande, S.: Deep learning based car damage classification. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 50–54. IEEE, 2017 Dec 18 2. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), Antalya, pp. 1–6 (2017) 3. Guo, T., Dong, J., Li, H., Gao, Y.: Simple convolutional neural network on image classification. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, pp. 721–724 (2017) 4. Moscato, P., Cotta, C., Mendes, A.: Memetic algorithms. In: New optimization techniques in engineering, pp. 53–85. Springer, Berlin (2004) 5. Soumalya Sarkar, M.G.M.R.G., Reddy, K.K.: Deep learning for structural health monitoring: a damage characterization application. In: Annual Conference of the Prognostics and Health Management Society (2016) 6. Tripathy, B.K., Sooraj, T.R., Mohanty, R.K.: Memetic algorithms and their applications in computer science. In: Computer Vision: Concepts, Methodologies, Tools, and Applications, pp. 1461–1482. IGI Global (2018) 7. Alsmadi, M.K.: An efficient similarity measure for content based image retrieval using memetic algorithm. Egypt. J. Basic Appl. Sci. 4(2), 112–22 (2017) 8. Lee, J., Kim, D.W.: Memetic feature selection algorithm for multi-label classification. Inf. Sci. 1(293), 80–96 (2015 Feb) 9. Li, Y., Hu, J., Jia, Y.: Automatic SAR image enhancement based on nonsubsampled contourlet transform and memetic algorithm. Neurocomputing 25(134), 70–8 (2014 Jun) 10. Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013) 11. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010) 12. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 580–587. IEEE (2014) 13. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016) 14. Redmon, J., Farhadi, A.: Yolov3: An Incremental Improvement, 2018 Apr 8. arXiv:1804.02767

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15. Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648–656 (2015) 16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012) 17. Simonyan, K., Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 18. Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010) 19. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. 1556 (2014). arXiv:1409.1556 20. Jayawardena, S. et al.: Image based automatic vehicle damage detection. Ph.D. dissertation, Australian National University (2013) 21. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, 4 Sep 2014. arXiv:1409.1556

Recent Applications of Swarm-Based Algorithms to Color Quantization María-Luisa Pérez-Delgado

Abstract Nowadays, images are very important elements in everyday communication. Current devices include high-quality displays that allow showing images with many colors. Nevertheless, the size of these images is a major issue when the speed of transmission and the storage space must be taken into consideration. The color quantization process reduces the number of different colors used to represent an image while trying to make the new image similar to the original. In addition to enabling visualization with low-end devices and efficient image storage and transmission, color reduction is related to other operations applied to images, such as segmentation, compression, texture analysis, watermarking and content-based image retrieval. The color quantization problem is complex, since the selection of the best colors to represent the image is a NP-complete problem. The complexity and interest of the problem have led to several solution approaches over the years. Recently, several interesting solutions have been proposed that apply swarm-based algorithms. Said algorithms are based on a population of individuals that cooperate to solve a problem. This chapter focuses on the swarm-based solutions proposed for the color quantization problem and shows that these novel methods can generate better images than those obtained by classical solution approaches. Keywords Color quantization


Swarm-based methods
Clustering

1 Introduction Images are very important elements in human communication. Currently available devices can define, capture, store and display high-quality images. Nevertheless, the human eye can only distinguish a limited range of colors. For this reason, some operations performed on images may consider a limited number of different colors M.-L. Pérez-Delgado (&) Escuela Politécnica Superior de Zamora, University of Salamanca, Av. Requejo 33, C. P. 49022 Zamora, Spain e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_5

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to represent the images. When an image is represented with fewer colors, its quality is reduced as well the space required to store the image. This second feature accelerates the transmission of said image in case such operation is required. Determining the optimal palette to reduce the colors of an image is a difficult problem [1]. For this reason, several solutions have been proposed that apply two different approaches: • Splitting methods, which consider the color cube and apply an iterative process that divides that cube into smaller boxes. The process ends when the quantity of boxes is equal to the number of colors that the method must define. Then, a color is selected to represent each box and the set of colors defines the quantized palette. Some popular methods of this type are those proposed in [2–6]. • Clustering-based methods, which apply techniques proposed to divide a set of items into several groups or clusters according to the features of said items. When the items to be clustered are the pixels of an image, the groups are defined so that they include pixels with a similar color. Some clustering methods that have been applied to reduce the colors of an image are K-means, competitive learning, neural networks and swarm-based algorithms. Swarm-based methods constitute a set of innovative techniques to solve difficult problems. The characteristics of these methods allow them to obtain good results for different types of problems. This chapter describes several methods of this kind recently proposed to deal with the color quantization problem. The description begins with the definition of the problem to be solved. Following, the main features of the swarm-based methods are presented. After this, several color quantization methods that use swarm-based solutions are described. The chapter ends with the conclusions.

2 Problem Definition A digital image is composed by a set of N pixels represented using a certain color space. When the RGB color space is considered, each pixel pi of the image, with 1  i  N, is represented by three integer values between 0 and 255, pi = (Ri, Gi, Bi), which indicate the amount of red, green and blue of the pixel. Therefore, this color space allows us to use 2563 different colors (i.e., more than 16 million colors). The color quantization process tries to reduce the number of different colors of the image without losing basic information. To achieve this goal, a color quantization method performs two operations. First, it selects a small number of different colors, q, to define a new palette, called quantized palette, C = {c1, …, cq}, where cj = (Rj, Gj, Bj). Next, the colors of this palette are used to determine the new image, called quantized image. To this effect, each pixel pi of the initial image is replaced by another pixel Pi, which takes one of the q colors from the quantized palette.

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3 Overview of Swarm-Based Methods In nature, there are some groups of animals that show some intelligence when dealing with certain problems. Although each individual performs very simple operations, the group itself can perform complex operations. For example, this type of behavior is observed when a group of birds or fish moves. Swarm-based methods define a group of computational algorithms that solve complex problems by imitating these groups of biological individuals. The behavior of different individuals has been imitated over the years: ants [7], particles [8], bacteria [9], frogs [10], fish [11], bees [12], cuckoos [13], bats [14], fireflies [15], wolves [16], etc. Swarm-based methods share some common features: • Each individual of the population has associated with some basic behaviors. • Although each individual only performs simple tasks, the combination of all of their operations allows the swarm to solve complex problems. • There is not a central control in these groups. • There is no individual access to the complete status of the swarm. • It is not possible to do an effective division of the work to be carried out. • Most of these methods were initially designed to solve an optimization problem. Therefore, there is an objective function associated with the problem to be solved and the solution method attempts to minimize (or maximize) that function. • The solution method combines exploration and exploitation. The method not only exploits the information already known related to solutions previously analyzed, but also explores new solutions. • Local and global searches are used to find a solution. The local search analyzes new solutions close to a given one, while the global search analyzes new solutions distant from that solution given. Several researchers have defined memetic algorithms by combining a swarm-based algorithm with another method. In this case, the swarm-based method performs the global search in the solution space and the other method applies local search to improve the solution obtained by the hybrid method. Some interesting methods of this type use the particle swarm optimization algorithm [17–19], the artificial bee colony algorithm [20–22] and the firefly algorithm [23–25].

4 Swarm-Based Methods Applied to Color Quantization This section describes several recent color quantization methods based on swarm intelligence. The methods that have been published including more details and results are described here more extensively, while the others are listed in a table.

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Some of the solutions evaluated use the K-means method. This is a well-known clustering method that defines k clusters of similar items. The algorithm begins considering k initial values called centroids, which can be selected randomly or by other techniques. After this, each item of the set is associated with the nearest centroid. Then, this algorithm calculates the average value of every cluster and these values are used as the new centroids. This process is applied iteratively during a preset number of iterations or until a predefined error is reached [26].

4.1

Artificial Ants

Several ant-based algorithms have been proposed that mimic different behaviors of natural ants. The ant-based methods used to solve the color quantization problem mimic two of these behaviors. This section first describes the solutions that build a tree of ants to quantize the image and then considers other methods in which the ants move on a grid to form groups of similar pixels.

4.1.1

The Ant-Tree for Color Quantization Method

Pérez-Delgado [27] describes a color quantization method called Ant-tree for color quantization (ATCQ), which adapts the Ant-tree algorithm to reduce the colors used in an image. The Ant-tree algorithm mimics the self-assembly behavior of some species of biological ants and applies it as a clustering technique [28]. The pixels of the original image are represented by ants that build a tree structure. The pixel pi is represented by the ant hi, with 1  i  N. Before applying the operations of the algorithm, the tree just includes the root node, a0, where all these ants wait to move through the tree until they become connected to it. The children of a0 are included in the second level of the tree as the operations progress. The number of children, q, is initially equal to 0 and it can increase to a predefined maximum, Qmax. When the algorithm ends, q will be the size of the quantized palette defined by the tree of ants. Every child Sc of a0, with 1  c  q, is likewise the root of a subtree of ants and has three variables attached to it: ncc, sumc and Dc. The variable ncc represents the number of ants connected to the subtree until this moment and sumc is the sum of the colors of all those ants. The color associated with Sc is sumc/ncc. On the other hand, Dc is computed by Eq. 1, where the value dic represents the similarity between the ant hi and the color of the node Sc when this ant was associated with the subtree whose root is Sc, dic = Sim (hi, sumc/ncc). Dc ¼

X hi 2 c

dic

ð1Þ

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The following box summarized the operations of the ATCQ method:

ATCQ algorithm while there are moving ants Take a moving ant, hi, which is on a node denoted apos if hi is on the root of the tree Apply operations to process an ant placed on the root of the tree else Apply operations to process an ant not placed on the root of the tree end-while Generate the quantized image The algorithm applies an iterative process that allows all the ants to be connected as nodes of the tree. Before applying this process, the ants are sorted by increasing average similarity, because the authors of the Ant-tree method suggested that better results are obtained with sorted data. At the beginning of the algorithm, the ants define a sorted list that is on the root of the tree. As the operations progress, the ants go down on the nodes of that structure until they connect to the tree, becoming new nodes. Each iteration of the algorithm takes an ant hi which is on the node apos of the tree. hi is considered a moving ant since it is not connected to the structure. The algorithm executes different operations depending on the node where the ant is. • If the ant is on the root of the tree, two different situations can happen. – In case the second level of the tree is empty, it is created the first node of this level and the ant connects to it. – In other case, the node Sc of the second level most similar to the ant is determined. Then, the similarity between the ant and the color of said node is compared to a threshold computed by Eq. 2, where a 2 (0, 1]. If the ant and the color of Sc are sufficiently similar (if dic  T), hi moves to the root of the subtree c; in other case, it must be included in a new subtree. In the special case in which the tree already includes Qmax nodes in the second level, no more subtrees can be created, so that the ant moves to the subtree c. T¼

Dc a ncc

ð2Þ

When a node Sj of the second level of the tree is created, the algorithm initializes the variables associated with that node: ncj and Dj are set to 1 and 0, respectively, and sumj takes the color of hi.

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When an ant moves on the root of an existing subtree c, the information related to the root of said subtree is updated: The color of the ant hi is added to sumc, dic is added to Dc and ncc increases by one. • If the ant is not on the root of the tree, there are three different possible situations: – When apos has no child, hi becomes the first one. – When apos has exactly two children and the second one has never been disconnected from the structure, this child and all its descendants are disconnected and return to the root of the general tree. Next, hi becomes the new second child of apos. – In all other cases, a child of apos, denoted a+, is chosen to determine if hi connects to the tree or moves down into the structure. If a+ and hi are not similar enough (Sim(hi, a+) < T) and apos can include at least one more child, hi becomes a new child of apos; in other case, hi moves on a+. The process concludes when all ants have been linked to the structure. At this moment, the quantized palette generated by the ants includes q colors, defined by the children of a0 (sum1/nc1, …, sumq/ncq). To define the quantized image, the pixels of the original image represented by all the ants included in the subtree c are replaced in the new image by the color sumc/ncc.

4.1.2

The Iterative ATCQ

Based on the ATCQ algorithm, [29] describes another color quantization method, called ITATCQ. In essence, ITATCQ applies iteratively the operations of ATCQ. The first iteration of ITATCQ performs the same operations as ATCQ and builds a tree. The following iterations use the tree defined in the previous iterations. Before performing a new iteration, all the ants are disconnected from the structure and only the root node and its children remain in the tree. Then, the ATCQ operations are applied again to connect all the ants to the tree. The main differences between both methods, ATCQ and ITATCQ, are these: • ITATCQ performs a preset number of iterations, each of which defines a quantized palette that allows obtaining a quantized image. As iterations progress, the quantized image improves. • ITATCQ does not consider sorted data. This algorithm does not use pixels sorted by average similarity. On the contrary, the pixels are processed in the same order they are read from the original image, that is, from left to right and from top to bottom. This feature reduces the execution time of the quantization process. • To accelerate the process, the disconnection of ants performed during ATCQ operations is eliminated. In this case, each iteration of ITATCQ processes each

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ant once, and this reduces the execution time. When this disconnection process is eliminated, it can be used a tree with just three levels connecting the ants in the third level of the structure. Next box shows the steps of the ITATCQ algorithm:

ITATCQ algorithm for t=1 to TMAX while there are moving ants Take a moving ant, hi, which is on a node denoted apos if hi is on the root of the tree Apply operations to process an ant placed on the root node of the tree else Connect hi to apos end-if end-while if t 90% and FM > 90%). On the basis of accuracy, the suitability of different algorithms for different land cover features for the Alwar region of Rajasthan has been given in Table 9. The algorithm possessing producer’s accuracy in the range of 60–90% for a land cover is considered as suitable (represented by “S”) for that land cover, algorithm having accuracy above 90% is considered highly suitable (denoted by HS) and algorithm having accuracy below 60% is labeled as not suitable (NS) for that particular land cover feature. From Table 9, we can say that the hybrid algorithms perform very well on water and rocky land cover features. All these algorithms can handle applications requiring water land cover feature efficiently as their overall accuracy is extremely high. The algorithms BBO-GS handle all land cover features efficiently except urban features. FPAB/BBO has the least accuracy for rocky, barren and vegetation features when compared to the accuracy of other algorithms for the respective land cover features. The algorithms which are favorable for urban feature include ACO2/ BBO, ACO2/PSO/BBO and ACO2/PSO/BBO-GS. FPAB/BBO is the only algorithm which is not very suitable to be used for barren land cover. Thus, most of algorithms considered classify the features like water, rocky and vegetation with decent accuracy. If considered on the basis of TSS and F-measure, most of the algorithms have especially high TSS and F-measure for water (close to 100) compared to the other features that are classified by them, and so if the land cover that is being classified is consisting of mainly water region, then above algorithms are safe for use. This is particularly useful for situations like groundwater detection and in disaster management during floods to provide accurate measures of effect of flood in a region. Urban and barren are among the features which are difficult to classify because their classification error is also very high approximately greater than 20%. Urban is the land cover feature on which most of the algorithms like rough fuzzy tie-up, membrane computing, BBO, extended BBO and FPAB/BBO fail to perform efficiently. BBO-GS-based extractor has greater than 10% of classification error for urban and barren. Producer’s accuracy and user’s accuracy are the other performance measures used for comparing the classifiers. The division of the count of pixels classified accurately in one class, and all the pixels as taken from data being referenced (i.e., the total of the column in the confusion matrix) give the value of producer’s accuracy. The level of accuracy up to which a specific region has been classified can be analyzed from producer’s accuracy. It can suffer from the error of omission that specifies the part of the checked features on the ground which have not been categorized in the map. The producer’s accuracy is inversely proportional to the count of errors of omission. Producer accuracy (%) = 100% − error of omission (%). The algorithms which are described above are compared on the basis of producer’s accuracy for all the five land cover features in Table 10. User’s accuracy can be calculated by dividing the count of pixels classified accurately in a class and the count of pixels which were examined in the specific class. The comparison of user’s accuracy of all the hybrid algorithms is given in Table 11.

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Table 10 Producer’s accuracy on various land cover features of Alwar Region (in %) Algorithm

Land cover features Water Rocky

Urban

Barren

Vegetation

ACO2/BBO ACO2/PSO/BBO FPAB/BBO BBO-GS ACO2/PSO/BBO-GS

100 100 100 98.5 98.5

100 98.9 47.3 34.7 100

98.1 94.5 61.7 94.7 98.1

95.3 97.3 92 94.6 97.9

99.4 97.4 90.5 92.5 99.4

Table 11 User’s accuracy on various land cover features of Alwar Region (in %) Algorithm

Land cover features Water Rocky

Urban

Barren

Vegetation

ACO2/BBO ACO2/PSO/BBO FPAB/BBO BBO-GS ACO2/PSO/BBO-GS

100 94.5 100 100 100

97.4 94.4 70.3 81.4 97.9

95.8 96.9 49.2 56.8 98.7

100 100 92 99.3 100

98.5 100 82.6 90.6 99

From Tables 10 and 11, we can see that the algorithm BBO-GS has a fairly good producer’s accuracy for barren land cover feature, but it has a poor user’s accuracy. It means that it classifies the actual barren pixels with a great accuracy, but it also gives a high false positive rate also, i.e., many pixels from other land cover features are considered as barren pixels. Similarly, FPAB/BBO also suffers from high false positive rate for barren land cover, and it has a low producer’s accuracy for barren features which means that the algorithm misses the actual pixels of barren land cover and pixels from other land covers are classified into barren, thus making FPAB/BBO a poor performing algorithm on barren land cover features. BBO-GS has a very poor producer’s accuracy, i.e., it is not able to capture the urban features correctly, but at the same time, it does not give high false positive rate also (user’s accuracy). Out of all the hybrid algorithms, FPAB/BBO is the algorithm which gives the highest false positive rate for all the land cover features when compared with other algorithms on the basis of user’s accuracy. Figures 11 and 12 give the graphical representation of producer’s accuracy and user’s accuracy, respectively. The algorithms can be used in various real-world applications like traveling salesperson problem, face recognition, groundwater detection, etc. on the basis of characteristics they possess and the requirements of the applications. The optimization characteristics of the algorithms include environment type, global optima, computational complexity, sharing of attributes, evolution type, image quality required, flexibility of objective function and convergence time. Environment type means whether the variables of the objective function are fixed or can be changed according to the application in which algorithm is being applied. Environment type is static if the variables cannot be changed as per application and dynamic

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Fig. 11 Producer’s accuracy of various algorithms

Fig. 12 Comparison of algorithms on the basis of user’s accuracy

otherwise. By global optima, we mean whether the algorithm is capable of finding the global optima for the problem or has chances of getting stuck into the local optima. Attribute sharing characteristic of an algorithm is yes if any kind of information sharing takes place between various agents or objects. Evolution type means the mechanism of how the algorithm works and the inspiration factor from which the mechanism is inspired. Image quality is a very crucial characteristic when dealing with the imagery dataset. Requirement of high-resolution images adds to

Dynamic Dynamic Dynamic Dynamic Dynamic

ACO2/BBO ACO2/PSO/BBO FPAB/BBO BBO-GS ACO2/PSO/ BBO-GS

Polynomial time Polynomial time Polynomial time Exponential time Exponential time

Characteristics Environment Computation Type complexity

Algorithm

Yes Yes Yes Yes Yes

Global optima

Table 12 Comparison of various characteristics of algorithms

Yes Yes Yes Yes Yes

Sharing of attributes Population based Population based Population based Geo-science based Geo-science based

Evolution type High Low Low Low Low

Image quality

Yes Yes Yes Yes Yes

Objective function flexibility

Fast Fast Fast Fast Fast

Convergence time

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extra cost overhead of an algorithm in the application areas where the dataset is paid. In contrast, the algorithms which can give comparable results using low-resolution images also can help in cutting this overhead and saving the memory usage for handling the large size high-resolution images. Flexibility of the objective function is the property of an algorithm to modify its objective function in order to achieve adaptability for any of the application it is being used for. Convergence time refers to the time taken by an algorithm to reach or converge to the required solution or the global optima of the problem. The characteristics and suitability of the algorithms for various applications are given in Tables 12 and 13, respectively. ACO has the advantage of performing parallel processing and sorting; thus, ACO2/BBO can be used in the applications requiring parallel computation and for job scheduling. The algorithm works extremely well for water and rocky land covers, thus making it favorable to be used for groundwater detection, finding suitable path of submarines and searching the safest location for military camps in hilly areas. BBO performs very efficiently for the water and rocky land cover feature, so BBO or any extended BBO is favorable to be used in the purpose of finding groundwater availability, searching optimal path for submarines and finding suitable place for military camps in hilly areas. Being able to avoid the trap of local optima and due to its dynamic computational environment, BBO and extended BBO can perform well for TSP, pattern and face recognition. The algorithm ACO2/PSO/BBO combines the characteristics and strengths of all the three methods (ACO, PSO, BBO) and thus is suitable for most of the application areas listed like TSP, finding location of military camps, finding path for submarines, parallel computing, job scheduling, groundwater detection and face and pattern recognition. BBO-GS can perform extremely well for satellite image-based and terrain-based applications (groundwater detection, submarine path, TSP, military camp location) as it performs best for all the geographic features. It can also perform well for the purpose of face and pattern recognition because of its capability to work with low-resolution images. ACO2/PSO/BBO-GS has the capability of being applied to all the listed applications as it combines the characteristics of two efficient hybrid algorithms, namely ACO2/PSO and BBO-GS. Time complexity of the algorithms is also of critical importance. In applications where quick decisions have to be taken to understand patterns, the algorithms that take less time to provide acceptable results are of much more importance as compared to the one which are providing accurate results. Military planning and disaster management are the application regions which require quick response time. Fuzzy and rough fuzzy tie-up take less time for solution, and though they may not be as accurate as some other techniques, these provide results in better time. ACO/ PSO- and BBO-based algorithms have high complexity because of the exponential relationships with the sample size. The algorithms belonging to the category of human mind-based modelization and swarm intelligence can reach to the solution in polynomial time. The algorithms which belong to the category of artificial immune system based and geoscience based take exponential time to reach near-optimal solutions.

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Table 13 Suitability of algorithms for various applications Algorithm

Applications Groundwater presence

Parallel computing

Military camp location

TSP

ACO2/BBO

✓

✓

✓

✓

ACO2/PSO/ BBO

✓

✓

✓

FPAB/BBO

✓

BBO-GS

✓

ACO2/PSO/ BBO-GS

✓

✓

Face recognition

Job scheduling

Pattern recognition

Submarine optimal path planning

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Some algorithms though may not be producing any promising results but could be extended or combined easily with other algorithms. Therefore, such algorithms provide much flexibility as its hybrid versions with other techniques could be created easily, e.g., BBO is used with ACO/PSO and also FPAB to produce much better results. Algorithms like fuzzy have limited scope in terms of extension, and hence its further improvement is difficult.

6 Conclusion The paper is an extensive study of the current nature influenced computational intelligence methods which are used for extraction of geo-spatial feature problems till date. A well-known metric kappa coefficient suitable for evaluating the classification performance in the area of remote sensing is adapted to perform the comparative analysis of the nature-inspired CI method results. The algorithms are also compared on the basis of producer’s accuracy, user’s accuracy and overall accuracy. On the basis of producer’s accuracy, FPAB/BBO is the algorithm having least accuracy for rocky, barren and vegetation land cover features and BBO-GS performs poor for urban features. On the basis of user’s accuracy, FPAB/BBO is the algorithm having least accuracy and high false positive rate for all the land cover features. We analyze the results by constructing an error matrix and applying various metrics on it. The classification metrics indicate the usefulness of different algorithms in different situations. From the analytical study of the results, it is reflected that nature-inspired CI-based classifiers are precise, sophisticated and capable classifiers as compared to the probabilistic classifiers. It is observed that the swarm intelligence and geosciences-based CI classifiers are more efficient than CI classifiers based on the human mind and the artificial immune system. Also, it is shown from the results of classification that the classifier which is the hybrid of all the three basic swarm intelligence techniques combined with the concepts of geosciences, i.e., the hybrid ACO2/PSO/(BBO-GS) is the most suitable classifier from among all the present nature-inspired CI-based classifiers. The results show that the

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hybridization produces a slight better result but of not much use. The hybrids created with the specific application in mind produces much better results. All the algorithms have been critically analyzed on the basis of producer’s accuracy, and their suitability has been observed for various land cover features of Alwar area. BBO and all its hybrid algorithms work efficiently for the rocky and water land cover. The human mind-based algorithms lack behind in performance as compared to nature-inspired algorithms. The algorithms are also compared on the basis of various optimization characteristics like environment type, global optima, evolution type, sharing of attributes, etc. Using their performance on land cover features and their characteristics, the algorithms are analyzed for their applicability on various other applications like face recognition, pattern recognition, TSP, parallel computing, groundwater detection, etc. FPAB when combined with BBO gives reasonable results for terrain specific applications like TSP, groundwater detection, military camp, etc. but it lacks in its overall performance when compared to other hybrid algorithms of BBO. The results given by the algorithms for land cover features and their characteristics make them suitable for various other application areas also like BBO which can work very well for finding optimal path of submarines, groundwater detection, military camp location, etc. because of its capability to handle water and rocky areas efficiently. The hybrid algorithms combine the characteristics of the best performing algorithms, thus making them suitable for most of the major application areas including parallel computing, face and pattern recognition, job scheduling and terrain specific applications.

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An Efficient Copy-Move Forgery Detection Technique Using Nature-Inspired Optimization Algorithm Anmol Gupta and Ishan Chawla

Abstract The effective and simplest technique to create digital forgeries is copy-move forgery where a part of an image is copied and pasted elsewhere within the same image. Many techniques have been developed to detect such forgeries in digital images. The most computationally effective and robust technique is based on speeded-up robust features (SURF) framework. However, such techniques cannot produce satisfactory results for some images. Sometimes, the matched points detected are not enough or are too less to prove that the image is not authentic. Also, false forgeries are detected in some cases. The main reason behind such a scenario is that the detection results of the SURF-based framework highly depend upon the value of parameters which are mostly determined with the user experience. The predetermined parameter values limit the application of copy-move forgery detection since they cannot be applied to all the images. Therefore, a novel approach is proposed in this paper in which SURF-based detection scheme is amalgamated with nature-inspired optimization, i.e., particle swarm optimization (SURF-PSO) algorithm. This approach utilizes particle swarm optimization for each image independently to generate the customized value of parameters for forgery detection under the SURF framework. The proposed approach is experimentally validated for copy-move forgery detection of a standard dataset. Based on the obtained results, it can be seen that as compared to the conventional SURF technique [based on predefined parameter values (SURF-PPV)], the proposed SURF-PSO outperforms in terms of higher precision and a lesser number of mismatched points.




Keywords Copy-move forgery detection Speeded-up robust features (SURF) Particle swarm optimization (PSO) Predefined parameter values







A. Gupta (&) Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India e-mail: [email protected] I. Chawla Department of Mechanical and Industrial Engineering, IIT Roorkee, Roorkee 247667, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_7

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1 Introduction In this era of digital computing, digital images are one of the most common and efficient mediums of communication. They have discovered their applications in the field of media, journalism, forensic studies, and law enforcement and are also used as a part of the evidence in courts. Due to this tremendous utilization of digital images and accessibility of various images editing software’s like Photoshop, GIMP, etc., it has become very easy to manipulate some significant contents or features of an image without leaving any visible clues and create a forgery that may change the entire semantics of an image. One of the most popular types of forgeries is copy-move forgery (CMF), where some part or region of an image is copied and then it is pasted elsewhere in the same image to create duplication. Therefore, to check the authenticity and integrity of images, digital image forensics has emerged as a research field [1]. The main aim of digital image forensics is to check if the given image is valid or not. Based on the literature survey, out of all the existing block-based and key-point-based methods, the most computationally efficient and robust method to different types of forgeries is speeded-up robust features (SURF). It is basically a modified and faster version of scale-invariant feature transform (SIFT). However, these key-point-based methods incur one common type of drawback, i.e., the detection results of SIFT [2] and SURF [3] are highly dependent on the various predefined parameters values (PPV). The technique that detects forged images by using SURF and these predefined parameters is named as SURF-PPV in this paper. These predefined parameters determined with user experience and provide good results only for a few images. Therefore, their application becomes limited when the key-points detected in an image are too less to say that the image is forged. Also, sometimes false identification of duplicated regions occurs, and true forged regions are not detected at all. To address this issue of predefined parameter values, a preliminary work based on SURF and PSO has been proposed in [4]. However, only the Hessian threshold parameter has been determined in that work. In this paper, an algorithm is proposed which utilizes particle swarm optimization (PSO) to determine various other parameters of SURF-based forgery detection (such as number of octaves, number of scales per octave, minimum distance between the matched key-points, etc.) along with the Hessian threshold for effective key-point matching and rejection of unstable key-points during copy-move forgery detection. The proposed technique is named as SURF-PSO, which identifies optimized and customized parameters automatically for each image separately. The proposed technique is validated by simulation results to effectively determine copy-move forgery.

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Related Work

Various copy-move forgery detection techniques have been developed until now. The most popular ones are scale-invariant feature transform (SIFT), Harris corner detector, speeded-up robust features (SURF), to name a few. Lowe [2] first introduced the concept of detecting scale-invariant difference of Gaussians (DoG) key-points and extracting feature descriptors using gradient orientation histograms. Hence, SIFT proved to be scale and rotation invariant. Also, it is robust against various geometrical transformations. However, it possesses high computational complexity and is also unable to detect forgeries in flat areas or regions with inconspicuous changes due to lack of detection of reliable key-points. Since key-point detection highly depends upon the values of the parameters used to accept or reject certain low contrast and edge key-points, Amerini et al. [5] and Lyu [6, 7] noticed the influence of values of parameters and used a specific set of parameters for detecting duplicated regions. The parameters were set according to the experiences which were only applicable to a few images. Li et al. [8, 9] applied a SIFT-based technique to the independent patches of a test image and then used those patches to detect the forgery. However, default values of parameters were used for key-point detection and descriptor extraction. In [10], the Harris corner detector is used to extract edges and corners from the regions of an image. Harris features provide consistencies in natural images and are also enhanced to increase the reliable key-point detection to detect the forgery. The state-of-the-art review of recently introduced copy-move forgery detection techniques has been presented thoroughly by Teerekanok and Uehara [11]. To improve the main drawback of the existing key-point techniques, i.e., high computational complexity, Bay and Ess [3] proposed SURF to reduce the processing time and the dimension of the feature vector. Later, in [12], Bo et al. extended this technique by increasing the length of the descriptor vector to 128. Although SURF improves the processing time in the detection of copy-move forgery, it still fails in some cases when a set of parameters is specified in such a way that very few key-points are detected. Hence, optimizing the values of these parameters according to the conditions present is one of the urgent needs of image forensics. The structure of the rest of the paper is as follows: The issues related to predefined parameter values are discussed in Sect. 2. The proposed algorithm named SURF-PSO is represented in Sect. 3. Section 4 provides the evaluation results of the proposed technique. Finally, a conclusion is drawn in Sect. 5.

2 Problem Formulation In this section, the brief introduction to SURF-based forgery detection technique and the various problems that arise due to predefined parameter values are discussed.

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The SURF-Based Framework

To prove an image to be unauthentic, it must go through various steps as described in Fig. 1. (1) During pre-processing, the test image is preprocessed in which the RGB image is converted into a grayscale image. (2) Key-point Detection involves detecting stable interest points that are invariant to scale, rotation, compression, contrast, etc. that is, the key-points must be stable to various geometric and illumination transformations. (3) Feature extraction is basically done to give a unique identification to each key-point. It is a region description method in which a small region around a key-point is utilized to represent it in the form of a description vector. (4) Matching includes determining key-points whose feature vectors are almost similar based upon the Euclidean distances among them or to identify those key-points which possess similar properties. The regions around the matched key-points are used to represent the duplicated regions. (5) Filtering is done to remove any mismatched key-points that are erroneously matched during the matching process. (6) Post-processing operations include estimating geometric parameters, applying morphological operations to find the boundaries of the matched key-points and locating the forged regions. The effect of the main steps of the copy-move forgery detection algorithm is shown in Fig. 2.

2.2

Problems in the Parameter Value Selection

Any set of predefined parameters is applicable only to a few images. But when it comes to detecting large dataset of images, certain limitations occur. Therefore, we

Fig. 1 Common flow graph of SURF-based forgery detection technique

Preprocessing

Postprocessing

Keypoint Detection

Filtering

Feature Extraction

Matching

An Efficient Copy-Move Forgery Detection … Fig. 2 Detection results under SURF-based framework

157

Pre-Processing

Feature Detection and Extraction

Matching

Visualisation

need to adjust these parameter values, when predefined parameters do not provide satisfactory results or when they are not suitable for a certain image. Since different researchers get different experiences while using these parameters; therefore, there is no unified standard for forgery detection parameters. These problems arise when: (1) The duplicated regions are not able to provide enough key-points for the matching criterion or the key-points detected are mostly unstable that they get filtered out in the process. (2) Some images might contain similar regions and those regions may be erroneously detected as duplicated ones, when parameter selection is not done accurately. Hence, it is possible that the native regions of an image are misclassified as forged ones. (3) The number of matched key-points is too less to prove any image to be forged one. Because in CMF detection techniques, a minimum of four pairs of matched key-points is necessary to prove that the regions are duplicated. Hence, forgery cannot be detected with very few or when no key-point is detected.

3 Proposed Algorithm Due to the problems in utilizing the predefined parameters for forgery detection, it is important to find optimal parameters for SURF-based forgery detection. To solve this problem, this section discusses the proposed approach, which utilizes particle swarm optimization to find optimal parameters for each test image. The schematic representation of an algorithm is depicted in Fig. 3. It primarily consists of two

158 Fig. 3 Flow diagram of the proposed SURF-PSO

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Initialize the Parameters Input Image Elemental Detection Conversion from RGB to Grayscale Image SURF Key-point Detection Descriptor Vector Extraction Key-point Matching Estimate Mismatched Key-points Estimate Affine Transform Parameters Estimation Analyzing the Detected Results from Previous Steps Generate a New Set of Parameters Run N times Output the results and the parameters

main steps: One is elemental detection, which is basically SURF-based framework that is used to detect a forgery in images and the other one is parameters estimation, which is used to provide satisfactory values of these parameters for each image separately. For the estimation of these parameters, particle swarm optimization (PSO) [13, 14] is employed in this paper. Initially, the predefined parameter values are given as an input to this algorithm to detect copy-move forged images then the following operations are run N times: (1) Elemental Detection detects the forgery in images with the parameter values applied, and then the detection results, i.e., the no. of matched key-points is delivered to the next round. The evaluation criterion is built based upon the detection result of the first round. (2) Parameter Estimation estimates the new set of parameter values according to the results obtained by the first step. Then the new set of parameters is delivered to step 1 to start the next round. Hence, the process goes on like this. After completing N rounds of execution, the parameters that give the best detection results are chosen for each image. Here, the value of N is decided to be 100.

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159

The Elemental Detection

After initializing the input parameters for a test image, the following five steps of elemental detection are performed that are almost similar to SURF-based framework described previously. In pre-processing, a true RGB image is simply converted to a grayscale image. Key-point Detection and Feature Extraction involve detecting points of interest from an image by utilizing the concept of integral images. After detecting key-points, the feature description method based on Haar wavelet filters is applied to represent each interest point in the form of a 64-D feature vector. SURF uses 64-D feature vector instead of the 128-D feature vector of SIFT, which makes it computationally much faster than SIFT. In matching, a method is suggested that computes the ratio of first and second nearest neighbors of every key-point and then it is compared with a threshold T (often fixed to 0.6). For every key-point, a similarity vector D ¼ fd1 ; d2 ; . . .dn g is defined in which the distance with respect to other key-points is listed in sorted manner, i.e., in ascending order. The condition for key-points to be matched is listed in Eq. 1: d1 \T d2

where T 2 ð0; 1Þ

ð1Þ

In this, d1 and d2 are the first and second elements of the sorted matrix for every key-point. This procedure is popularly known as a 2NN test. Finally, a set of matched points is obtained. Furthermore, in filtering, the matched points that are lying very close to each other need to be eliminated because they represent the areas that are similar but not copied. This elimination is done by computing the Euclidean distance between the coordinates of matched descriptors. Only those matches are retained for which the computed distance is greater than some threshold value denoted by Dmin and are saved for further processing. The other mismatched key-points are removed by estimating the geometric transformations between pairs of matched key-points. The geometric relationship, described by H which is a 3  3 matrix, between two matched points (a, b) and (a′, b′) is computed as shown in Eq. 2: 2

3 2 3 a0 a 4 b0 5 ¼ H 4 b 5 1 1

ð2Þ

At least three matched points are needed to compute this matrix. However, the estimated homography can be severely disturbed by mismatched points (outliers). For this reason, random sample consensus algorithm (RANSAC) [15] is presented to perform the previous estimation. In this, a set (usually three pairs) from the matched points is selected to estimate the homography H, then all the remaining

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points are transformed according to H and comparison is done with respect to their corresponding matched points in terms of distance. Inliers or outliers are catalogued according to this distance if it is below or above a certain predefined threshold b respectively. A number of iterations Niter are listed and then the estimated transformation which gives the highest number of inliers is chosen. Here, Niter is fixed to 1000 and the threshold b has been set to 0.05.

3.2

The Parameters Estimation

For parameters estimation, particle swarm optimization (PSO) is applied, which is used for solving minimization and maximization problems. Before applying PSO, two things are needed to be considered. (1) Which parameters are needed to be optimized? (2) Formulation of the evaluation function to select the optimized parameters.

3.2.1

Parameters of Optimization

Different values of different parameters make a large impact on detection results. The parameters that are needed to be optimized and their decision boundaries are listed in Table 1. The parameter values that are used in the implementation of SURF are different in different kinds of literature: O, S, and Thresh: These parameters play a useful role in key-point detection stage. O specifies no. of octaves; S specifies no. of scales per octave and Thresh is used to reject unstable key-points. OpenCV [16] implementation of SURF uses O = 4, S = 2 and Thresh = 0.05. MATLAB [17] implementation of SURF chose O = 3, S = 4, and Thresh = 0.1000. According to these, the domains of these parameters are chosen as O
[1, 4], S
[2, 6], and Thresh
[0.0001, 0.1000] in this paper.

Table 1 Optimization parameters (parameters in SURF-based algorithm) Parameters

Definition

Lower bound

Upper bound

O S Thresh Ʈ Dismin

No. of octaves No. of scale levels per octave The threshold for rejecting unstable key-points The threshold for key-point matching The minimum distance

1 2 0.0001 0.5 10

4 6 0.1000 0.8 60

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Ʈ: This parameter is utilized in key-point matching. Ʈ = 0.5 is used by Pan et al. [7], Amereni [5] chose Ʈ = 0.6 and Costanzo [18] set Ʈ = 0.8. Hence, the domain is set as Ʈ
[0.5, 0.8] in this paper. Dismin: This parameter is used in removing mismatched key-points in the filtering stage. Christlein [19] set the value of this parameter as Dismin = 50. Hence, its domain is set as Dismin
[10, 60] in this paper.

3.2.2

Evaluation Function

Although different kinds of literature use different metrics to evaluate the performance of detection techniques, the key idea is similar. The more the number of truly matched points (TMKs) and the less the number of mismatched points (MMKs), the more effective is the detection result. Therefore, while building the evaluation function, these factors are considered to be most important. When the number of truly matched key-points (TMKs) is large, the duplicated regions are not only estimated accurately but also the detection results become more convincing. Hence, the evaluation function is described in Eqs. 3 and 4 as follows: Pm ¼ TMKt=ðTMKt þ dÞ  MMKt; MMKt [ 10 d¼ 10; MMKt  10

ð3Þ ð4Þ

where TMKt and MMKt are not the number of truly matched or mismatched key-points, respectively. They are both determined by the affine transform in the filtering section. TMKt are the truly matched key-points that satisfy the affine transform described in Sect. 3.1. The other pairs that do not satisfy this transformation are regarded as mismatched key-points (MMKt).d is a mismatch coefficient and it provides a value, i.e., a default minimum value to MMKt to reflect real matching. Pm is the probability of matching. The parameters yielding highest value of Pm are chosen. Hence, the criterion for evaluation of SURF-PSO is Pm.

4 Results and Discussion The SURF-PSO is implemented in MATLAB 2015a and the results are compared with standard SURF-PPV. The database provided by Christlein et al. [19] is utilized here to check the performance of SURF-PSO. This database consists of 48 forged images with an average size of images equal to 3000 * 2300 pixels. Five examples are used in this experiment as shown in Fig. 4. Some settings for the PSO algorithm that are made at parameters estimation step are as follows.

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(a1)

(a2)

(a3)

(a4)

(a5)

(b1)

(b2)

(b3)

(b4)

(b5)

Fig. 4 Five examples utilized in the experiment. (a1–a5) Forged images; (b1–b5) Ground truth images

The maximum numbers of iterations are 100 and the size of the population is 20. The inertia range is set to 1.1. Both self and social adjustment weights are set to 1.49. The fitness function of PSO is given in Eq. 5 as Pf ¼ 1=Pm

ð5Þ

The main difference between SURF-PPV and SURF-PSO lies among the values of the parameters only. Choosing different parameters to yield different results. Hence, parameter selection is the main criterion for improving the detection of copy-move forged images. Therefore, the results of both the methods are compared on the basis of TMKs and the precision between them is calculated as shown in Eq. 6: Precision ¼ TMKs=ðTMKs þ MMKsÞ

ð6Þ

where TMKs are the truly matched key-points and MMKs are the mismatched key-points as determined by RANSAC in the filtering section. The visualization results of these images are shown in Fig. 5 by using SURF-PPV and SURF-PSO techniques. The detection results of these images typically demonstrate the futility of detection of copy-move forgery using SURF-PPV. In Fig. 5a1, it is clearly shown that the SURF-PPV is not able to reveal the duplicated regions at all, while SURF-PSO can reveal the same accurately and effectively as shown in Fig. 5b1. Figure 5a2 shows that the matched points detected by SURF-PPV are too less to accurately mark the forged regions. Using particle swarm optimization (PSO), the number of truly matched key-points increases significantly and hence the detection results are improved as can be seen in Fig. 5b2. In Fig. 5a3, although SURF-PPV can detect some true forged areas, it also detects enough false matched points to misinterpret the authentic regions as forged ones.

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(a1)

(a2)

(a3)

(a4)

(a5)

(b1)

(b2)

(b3)

(b4)

(b5)

Fig. 5 Comparison between detection results of SURF-PPV (a1–a5) and SURF-PSO (b1–b5)

SURF-PSO is also applied to detect multiple copy-move forgeries as shown in Fig. 5b4. The results show that the SURF-PPV method is not able to detect enough matched points to mark the regions as forged ones. Hence, with only 10 matched points detected in different regions of multiple forgeries, one cannot infer that those regions are not authentic. Therefore, we can say that no forgery is detected in this case. However, SURF-PSO detects enough matched points in each region separately and hence provides efficient detection results. Similarly, in Fig. 5a5, it is shown that no forgery is detected in case of SURF-PPV whereas the results obtained by SURF-PSO are correct and accurate because it improves the detection by increasing the flexibility in choosing parameter values. Furthermore, the comparison results of five images based on truly matched key-points (TMKs) and precision between SURF-PSO and SURF-PPV are shown in Fig. 6. Also, the mathematical results of these five images are listed in Table 2 which indicates the name and size of different images used, TMKs, MMKs and the precision of SURF-PPV, SURF-PSO and CMFD-PSO [20]. Based on the results, it can be said that the detection results of SURF-PSO are better than SURF-PPV.

(a)

SURF-PSO

1000 500 0

(b)

SURF-PPV

1

2 3 4 Image Number

5

SURF-PSO

1.5 Precision

TMKs

1500

SURF-PPV

1 0.5 0

1

2 3 4 Image Number

5

Fig. 6 a Comparison between TMK’s detected by SURF-PSO and SURF-PPV; b Comparison of Precision between SURF-PSO and SURF-PPV

0.005, 0.012, 0.020, 0.045, 0.015,

0.57, 0.59, 0.62, 0.60, 0.56,

50 55 40 50 60

4, 4, 4, 4, 4,

Four_babies (1024  681) Beach_wood (1024  768) Window (1024  768) Jellyfish (1296  1944) Hedge (1224  1632)

6, 5, 6, 5, 6,

CPV (O, S, Thresh, Ʈ, Dismin)

Image (size) 0 132 64 10 0

0 0 64 2 8

SURF-PPV TMKs MMKs

Table 2 Comparison among SURF-PPV, SURF-PSO, and CMFD-PSO

0 1 0.5 0 0

Prec. 336 1064 260 48 616

0 2 0 2 0

SURF-PSO TMKs MMKs

1 0.998 1 0.96 1

Prec.

1500 5112 190 – –

3 2 0 – –

CMFD-PSO [20] TMKs MMKs

0.998 0.999 1 – –

Prec.

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Using PSO algorithm, SURF-PSO can choose different suitable and optimized parameters for each image separately. Hence, SURF-PSO not only detects more images than SURF-PPV but also increases the number of truly matched key-points and hence improves the precision. The results are also compared with CMFD-PSO technique presented in [20] where the multiple copy-move forgery is not taken into account. Also, the matched key-points detected in that case are more which clearly means that the computational complexity is more. But the method proposed in this paper is computationally less complex and also deals with multiple copy-move forgeries.

5 Conclusions An approach based on speeded-up robust features (SURF) along with particle swarm optimization (PSO) has been presented in this paper under the name SURF-PSO to detect copy-move forgeries in digital images effectively. The predefined parameter values utilized in SURF-based forgery detection scheme exhibit various drawbacks due to which false forgeries are detected in many cases. Therefore, these predefined parameters are tuned by amalgamating SURF with PSO to get reliable and authenticated results. The proposed methodology has been validated by simulation results. The obtained results show that the SURF-PSO can automatically generate optimized parameters for each image separately, without making use of experiences and experiments. The proposed approach has also been proved to be effective, when SURF-PPV is unable to detect any matched key-points as well as when the truly matched key-points are too less to mark the duplicated regions accurately. Hence, it can be concluded that as compared to the conventional SURF technique (SURF-PPV), the proposed SURF-PSO outperforms in terms of higher precision and a lesser number of mismatched points.

References 1. Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009) 2. Lowe, D.G.: Distinctive image features from scale-invariant key-points. Int. J. Comput. Vision 60(2), 91–110 (2004) 3. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008) 4. Muzaffer, G., Guzin, U., Eyup, G.: PSO and SURF based digital image forgery detection. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 688– 692. IEEE (2017) 5. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)

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6. Pan, X., Lyu, S.: Detecting image region duplication using SIFT features. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp 1706– 1709. IEEE (2010) 7. Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. Inf. Forensics Secur. 5(4), 857–867 (2010) 8. Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015) 9. Yang, B., Sun, X., Guo, H., Xia, Z., Chen, X.: A copy-move forgery detection method based on CMFD-SIFT. Multimed. Tools Appl. 77(1), 837–855 (2018) 10. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 50, p. 10.5244. Citeseer (1988) 11. Teerakanok, S., Uehara, T.: Copy-move forgery detection: a state-of-the-art technical review and analysis. IEEE Access 7, 40550–40568 (2019) 12. Bo, X., Junwen, W., Guangjie, L., Yuewei, D.: Image copy-move forgery detection based on SURF. In: 2010 International Conference on Multimedia Information Networking and Security (MINES), pp 889–892. IEEE (2010) 13. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS’95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE (1995) 14. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International of First Conference on Neural Networks, Perth, Australia. IEEE Press (1995) 15. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981) 16. Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCV library. O’Reilly Media, Inc., Sebastopol (2008) 17. Documentation, M.: The MathWorks Inc (2005) 18. Costanzo, A., Amerini, I., Caldelli, R., Barni, M.: Forensic analysis of SIFT keypoint removal and injection. IEEE Trans. Inf. Forensics Secur. 9(9), 1450–1464 (2014) 19. Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012) 20. Wenchang, S., Fei, Z., Bo, Q., Bin, L.: Improving image copy-move forgery detection with particle swarm optimization techniques. China Commun. 13(1), 139–149 (2016)

Design and Implementation of Hybrid Plate Tectonics Neighborhood-Based ADAM’s Optimization and Its Application on Crop Recommendation Lavika Goel, Navjot Bansal and Nithin Benny

Abstract Agriculture is the primary employment of people in India. Even after years of experience in farming, farmers are not able to take the right decision when it comes to the crop selection. This paper proposes to use plate tectonics neighborhood-based classifier along with Adam’s algorithm for more optimized results. The final aim of this paper is to create an algorithm that will correctly determine the most suitable crop to be grown in a particular region given various input factors. As part of the paper, plate tectonics-based optimization is used in combination with Adam’s algorithm, and a hybrid technique is developed from it. The hybrid is tested on the benchmarks to compare its convergence to global minima in comparison with its predecessor PBO. The classifier that was developed is employed for finding the best class of crops that can be grown in a place given a set of features associated with the place. We also test our proposed algorithm on soybean dataset to predict disease class based on the various symptoms leading to the disease. The Chapter also emphasizes accuracies found on few datasets that are collected online or collected independently. Of the data collected online, it was found to be 90% accurate and had a Cohen Kappa Score of 0.84 from 1 while for the data collected independently, it was 98% accurate and had a Cohen Kappa Score of 0.984 from 1.




Keywords Plate tectonics-based optimization (PBO) Adam’s optimization algorithm Ada grad (adaptive gradient descent) Stochastic gradient descent (SGD) Plate accelerating index (PAI)










L. Goel (&)
N. Bansal
N. Benny Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Rajasthan, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 D. J. Hemanth et al. (eds.), Recent Advances on Memetic Algorithms and its Applications in Image Processing, Studies in Computational Intelligence 873, https://doi.org/10.1007/978-981-15-1362-6_8

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1 Introduction Agriculture, with its related sectors, is the largest source of livelihood in India. 70% of rural households continue to live mainly from agriculture, with 82% of farmers being small and marginal. In 2017–18, total food grain production was estimated at 275 million tons. The major motivation behind the paper origins from the fact that the soil quality is not always the same nor the weather and environmental conditions affecting the crops growth remains same over the course of the year. Thus, this problem opens up a huge platform for the implementation of machine learning in the field of agriculture. With the automation of the crop selection by feeding in the features of the soil and the historical data of the weather, the farmer will be able to figure out the crop that will get the best yield for the given soil type. Not all soil types are same; different soils have different features like pH, porosity and coarseness which contributes to the factor of what major crops can be grown in the farms that can provide the farmer a good yield. This gives us an opportunity to develop optimal classification models with higher accuracies to provide farmer with better opinions on crop selection during a particular season like Kharif or Rabi. There are certain crops that grow all around the year, thus providing additional benefits to the famers. The paper has an optimizer that is built upon certain independent algorithm with the Adam’s algorithm. Adam’s algorithm was used instead of Stochastic Gradient Descent (SGD) as the algorithm provided optimized evaluations which are tested on CEC 2017 benchmarks. A K Nearest Neighbor (KNN) algorithm is used to build the classifier that uses the relative forces acting on the plate to find out the stabilizing factors as weights in the training set. The classifier is trained upon a soybean dataset collected from online sources and a crop dataset collected independently. The accuracies of the dataset have been calculated using Kappa coefficient methods along with certain information on the dataset and the matrix that contains the produced results in the results section.

2 Related Work In 2013, a software program called SELECT was developed. It is a microcomputer-based decision support system for selecting crop varieties. This software relates characteristics of variety and experimental characteristics with actual performance of variety. It intends to support producers, country agents and other crop consultants in assessing adaptation of crop variety and performance, using data accumulated from evaluation trials conducted by the agricultural experiment station. It also provides information about production of crops and its varieties to producers. Tables 1 and 2 summarize the discussion above. In 2016, Chiranjit Singha et al. wrote a review paper. This approaches the problem of crop selection as land suitability study for the purpose of crop suitability. This paper also proposes the use of GIS for crop selection, by using tools to

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Table 1 International review of crop management practices Expert system for crop management

Description

Expert system

This helped in selecting the variety of soybean that can be grown after asking a series of questions from users This system helped the farmers and field advisors to select the variety of wheat in Scotland This aimed to select both winter wheat and soybean variety

SELECT Double Cropping expert system COMAX CROPLOT

This system advises the cotton growers on crop management The developed system aimed at determining the suitability of a particular crop with respect to a given plot

Table 2 Similar research work done Research paper

Characteristics

Use of machine learning

Gaps

Statistical and neural methods for site-specific yield prediction (2003)

Crop yield has been predicted using stepwise multiple linear regression (SMLR), feed-forward neural network Soil moisture which is a key feature of the soil has been estimated using remote sensing

Neural network

Complex system with a lot of manual efforts has been made

No

WEKA named software was used to sense the correct crop for that particular season

Yes

No machine learning is used and the system tends to seem complex GIS is not enabled and has very less features

Estimation of soil moisture using multispectral and FTIR techniques (2015)

Crop selection method based on various environmental factors using machine learning (2017)

perform actions and manipulation of spatial data for crop selection. The paper also analyzes the use of Analytical Hierarchy Process for assessing the relative suitability of factors. In 2016, Zhixu Qiu proposed a work which uses genomic selection (GS)—a breeding strategy which selects individuals with high breeding value to build a prediction model for maize. A bioinformatics pipeline is developed to perform machine learning-based classification for GS. A random forest-based classifier is used by the authors of the papers on the maize dataset.

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3 Plate Tectonics Neighborhood Optimizer with Adam’s Hybrid The algorithm borrows quite a few concepts from plate tectonics and mathematically formulates it to optimize arbitrary d-dimensional functions when it is solved. In addition to finding a local minima in the d-dimensional hyper-dimensional plane, it also incorporates the use of stochastic gradient descent to move towards a slope that produces steeper slopes. The most important aspect the algorithm accounts for is that after every iteration, the algorithm adds a Laplacian value to the PMI; thus, it allows the algorithm to check whether another minima is feasible even when the local minima is reached. This alternative algorithm built is an Adam’s optimizer crossed over the PBO optimizer. The algorithm had incorporated few concepts from the PBO and was implemented over by Adam’s algorithm to create a PBO Adam’s optimizer. Due to the incorporation of the Adam’s algorithm on the local minima aspects of PBO algorithm on d-dimensional hyperplane matrix, we observed that the Adam’s avoided any case of overshooting that happened at PBO alone and reached the global minima faster in less no. of iterations, thus creating a better pair for optimization purposes. The best part about the algorithm is that it incorporates the bias in the Adam’s section as well, thus adding two-way support to reach global minima (basically increasing the probability of the decreasing slope of the graph over each epoch). Figure 1 describes the algorithm explained.

3.1

PBO Adam’s Hybrid Pseudo-Code

PBO Adam’s hybrid tends to enhance the optimization capabilities of the optimizer, thus providing a better base for the classification model. The algorithm has a very simple purpose: move toward the stability, i.e., minima. The pseudo-code of the algorithm is intended to find a point in the plate section that has the lowest plate mobility index. The PMI is a depiction of a stable point, i.e., a point of global minima. The PMI is calculated using the goodness values, and to randomize the point choosing criteria, a random number is generated for the purpose of choosing the point. The algorithm is derived mainly from [2] with certain layering references from [3–7] on optimizing the algorithm to reach the global minima efficiently.

3.2

Comparison of Hybrid of PBO SGD and Hybrid of PBO Adam’s

Instead of using the gradient descent for the slope/‘gradient’ calculation and bias assignment, we used the Adam’s algorithm.

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1. If there is no candidate solution, generate the candidate solutions within [min limit , max limit] 2. Initialize the goodness value giving 1 to every candidate solution. 3. Find the Plate Mobility Index for all the points initialized. 4. Initialize max PMI of each point as the current PMI of that point. 5. Find out the radius value. 6. While Plate Mobility Index > 0 a. Mutated candidate solutions initialized = [ ] b. Loop for each point i. If f(chosen point) average of f Add the point to mutation array Else, Append it to the probable solutions list Randomly initialize a point if a point lies between 0 and upper limit of random else increase goodness += 10,000 v. If f(point) > average of f and PMI[i] < C1 > occr = 3 means that the ruleset X is the number of rows in T that matches the itemsets, and along with class label C1. Then the ruleset must evaluate with minimum sup-count threshold for frequency given in Eq. (1) Support count ¼ RUC

3.2

ð1Þ

Evolutionary Memetic Associative Classification

The proposed chapter aims to optimize the local and global search space through memetic algorithm that has been used in weight calculation using random walker. This will help the physician for better medication and quality treatment. • Random Walk in Associative Classification This section aims to explain the random walk algorithm in AC. Table 1 Dataset sample

Dataset ID

Attribute 1

Attribute 2

Class C

100 200 300 400 500

A1 A1 A2 A2 A1

A3 A4 A5 A4 A6

C1 C2 C3 C2 C1

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Procedure: A Typical hybrid Genetic Algorithm (GA) Input: Training data and test data Output: Class rule sets Begin 1. 2. 3. 4.

From the order the nodes of given attributes Construct the transaction matrix M based on node’s degree Initialize the probability b Assign k = 1 a. b. c. d.

Choose the random walker initial node N Initialize column vector eN 1 for node N and 0 elsewhere Let assign V column vector probability to walker Let assign V′ to each column vector probability to each round V 0 ¼ bMV þ ð1  bÞeN e. Determine appropriate attribute weight

5. Converged: Yes: Stop and assign weights to each catogories No: Go to step 4 6. S′ = empty set 7. Sr = generate all class rules 8. Weighted support = weight * support 9. If weighted support > minimum_support 10. S′ = S′ + Sr

4 Sample Computation Following working example is used to illustrate how the proposed memetic algorithm works for heart disease dataset. The same computation can be applied to any other domain. Let’s assume dataset (D) as shown in Table 2 with five training objects.

Table 2 Dataset sample

Age

Sex

Chest pain type (CP)

Heart disease

Senior Youth Junior Senior Junior

Male Male Female Female Male

Asymptomatic Angina Non-anginal pain Non-anginal pain Atypical angina

Yes Yes Yes No No

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Fig. 1 Bipartite graph Senior

Youth

Yes

Junior

No

Example 1 Consider the sample dataset shown in Table 2. It can be equivalently represented as a simple network social graph involving three levels under age attribute namely senior, youth, and junior and two class labels, ‘yes’ and ‘no’ given in Fig. 1. Senior and junior have connected with both the class labels, while youth has associated with only the ‘no’ class. Intuitively, we expect that junior is more similar to senior than youth, and an analysis using random walker at senior will support that perception. A bipartite graph also called bigraph shown in Fig. 1 is a graph used to form a two disjoint distinct network graph for a given problem such that no two network nodes lie in a same group. This graph can be used in complex graph collaboration network problem. For example, a user at a YouTube site places a tag on basketball. Thus three kind of distinct network will be formed user account, tags and web pages. Furthermore, these forms may be connected if they tended to use same tags frequently or tended to tag the same pages. Similarly, tag could be considered related if they appeared on the same pages or used by the same users, and pages could be visited by many users or consisting of many tags. Let us arrange the nodes as senior, youth, junior, yes, no. Then the equivalent transaction matrix for the network graph of Fig. 1 is 2

0 6 0 6 6 0 6 4 1=2 1=2

0 0 0 1 0

0 0 0 1=2 1=2

1=3 1=3 1=3 0 0

3 1=2 0 7 7 1=2 7 7 0 5 0

For example, fifth column corresponds to the network node ‘no’. It has degree of two since this node connects two categories of nodes namely senior and junior. The age categories nodes correspond to the first three rows and columns. So we assigned the degree 1/3. The class ‘no’ does not have an edge itself (or) the class ‘yes’, hence the last two entries in both fourth and fifth column marked as 0. Let us assume the walker probability b that continue at random in a network graph, so 1 − b has been teleport to the initial node N. eN in column vector that reflects the probability of the walker in each stage. Let eN has value ‘1’ in the row for node ‘N’ and ‘0’ if absence of node. Let ‘V’ is the probability the walker moving at each node of the next round from V.

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Example 2 Let us assume b is 0.8 and M is the equivalent matrix of Example 1. The random walker N is for node senior; now, we need to find the similarly of other categories to senior. Then we derive the equation for the new value V′ is 2

0 0 6 0 0 6 V1 ¼ 6 0 0 6 4 2=5 4=5 2=5 0

0 0 0 2=5 2=5

4=15 4=15 4=15 0 0

3 3 2 1=5 2=5 6 0 7 0 7 7 7 6 7 7 2=5 7 V þ 6 6 0 7 5 4 0 5 0 0 0

The initial vector V sum to 1; now, we can add the 1/5 with equation to each entries in the first row of the matrix M. then, the final first iteration of V′ would be 2

1=5 6 0 6 V10 ¼ 6 6 0 4 2=5 2=5

1=5 0 0 4=5 0

1=5 0 0 2=5 2=5

7=15 4=15 4=15 0 0

3 3=15 0 7 7 2=15 7 7 0 5 0

0:2 0 0 0:8 0

0:2 0 0 0:4 0:4

0:466 0:266 0:266 0 0

3 0:6 0 7 7 0:4 7 7 0 5 0

i.e., 2

0:2 6 0 6 V1 ¼ 6 6 0 4 0:4 0:4

Example 3 Let us apply the same assumption b to all other nodes. Then we derive the equation for the new value V2, V3, V4, and V5 respectively. Let us assume b = 0.8 for V2 2

0:16 6 0 6 V2 ¼ 6 6 0 4 0:32 0:32

0:16 0 0 0:64 0

0:16 0 0 0:32 0:32

0:373 0:213 0:213 0 0

3 0:48 0 7 7 0:32 7 7 0 5 0

We can add the 1/5 i.e., 0.2 with equation to each entries in the second row of the matrix M

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2

0:16 6 0:2 6 V20 ¼ 6 6 0 4 0:32 0:32

0:16 0:2 0 0:64 0

0:16 0:2 0 0:32 0:32

0:128 0:16 0 0:512 0

0:128 0:16 0 0:256 0:256

0:373 0:466 0:466 0 0

3 0:48 0:2 7 7 0:32 7 7 0 5 0

Let us assume b = 0.8 for V3 2

0128 6 0:16 6 V3 ¼ 6 6 0 4 0:256 0:256

0:299 0:373 0:373 0 0

3 0:384 0:16 7 7 0:256 7 7 0 5 0

We can add the 1/5 i.e., 0.2 with equation to each entries in the third row of the matrix M 2

0128 6 0:16 6 V30 ¼ 6 6 0:2 4 0:256 0:256

0:128 0:16 0:2 0:512 0

0:128 0:16 0:2 0:256 0:256

0:299 0:373 0:573 0 0

3 0:384 0:16 7 7 0:456 7 7 0 5 0

0:103 0:128 0:16 0:409 0

0:103 0:128 0:16 0:205 0:205

0:234 0:298 0:458 0 0

3 0:307 0:128 7 7 0:365 7 7 0 5 0

Let us assume b = 0.8 for V4 2

0103 6 0:128 6 V4 ¼ 6 6 0:16 4 0:205 0:205

We can add the 1/5 i.e., 0.2 with equation to each entries in the fourth row of the matrix M 2

0:103 6 0:128 6 V40 ¼ 6 6 0:16 4 0:405 0:205

0:103 0:128 0:16 0:6 0

0:103 0:128 0:16 0:4 0:205

0:234 0:298 0:458 0:2 0

3 0:307 0:128 7 7 0:365 7 7 0:2 5 0

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Let us assume b = 0.8 for V5 2

0:08 6 0:102 6 V5 ¼ 6 6 0:128 4 0:324 0:164

0:08 0:102 0:128 0:48 0

0:08 0:102 0:128 0:32 0:164

0:187 0:238 0:366 0:16 0

3 0:307 0:128 7 7 0:365 7 7 0:2 5 0

We can add the 1/5 i.e., 0.2 with equation to each entries in the fifth row of the matrix M 2

0:08 6 0:102 6 V50 ¼ 6 6 0:128 4 0:324 0:364

0:08 0:102 0:128 0:48 0:2

0:08 0:102 0:128 0:32 0:364

0:187 0:238 0:366 0:16 0:2

3 0:307 0:128 7 7 0:365 7 7 0:2 5 0:2

The sequence of estimation of the distribution of the walker that we get the weight for each category in the given dataset is 2 3 1 607 6 7 7 V1 ¼ 6 6 0 7; 405 0

2

3 1=5 6 0 7 6 7 6 0 7; 6 7 4 2=5 5 2=5

2

3 2 3 35=75 0:345 6 8=75 7 6 0:066 7 6 7 6 7 6 20=75 7. . .6 0:145 7 6 7 6 7 4 6=75 5 4 0:249 5 6=75 0:196

Before starting of class rule generation the corresponding weight is assigned to each attributes. For instance, the weight of (senior ! yes) would be 0.6 (senior = 0.345 + yes = 0.249). These weights are used to prioritize the disease; after the weight assignment, the algorithm will start generating frequent single itemset rules. Table 3 illustrates the suggested weights of categories in age attribute for the sample heart disease dataset given in Table 2. The following is the step by step illustration on how weighted associative classification works; the weight of each attribute will generate from random walker algorithm.

Table 3 Weight for age attribute

Categories of age attribute

Weights

Senior Youth Junior Yes No

0.345 0.066 0.145 0.249 0.196

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S1: Generate all single itemset rule S2: Compute support and weighted support – Weighted support (r) = weight (r1) * support (r1) – Weight (senior ! yes) = 0.345 * 2 = 0.69 S3: Compare the weighted support with minimum support for frequent single itemset S4: Similarly generate all combination of itemsets S5: Keep all the rules that satisfy the minimum confidence and eliminate the remaining CAR from the classifier. S6: One can predict for unknown instance; the algorithm finds the matches from CAR whose confidence is higher than other rules. S7: If found map the class label of CAR with unknown instance. S8: Calculate the accuracy of the algorithm. Generate all single candidate ruleset based on support and weighted support represented shown in Table 4. Frequent single ruleset with weighted support is shown in Table 5. The same rule generation process will follow for all the possible combination based on the given dataset.

Table 4 Single candidate ruleset

Single Class rules

Support

Weighted support

Senior ! yes Senior ! no Youth ! yes Youth ! no Junior ! yes Junior ! no Male ! yes Male ! no Female ! yes Female ! no Asy. ! yes Asy. ! no Angina ! yes Angina ! no Non-ang. ! yes Non-ang. ! no Aty. angina ! yes Aty. angina ! no

1 1 1 0 1 1 2 1 1 1 1 0 1 0 1 1 0 1

0.6 0.5 0.9 0.8 0.4 0.4 0.8 0.6 0.7 0.6 0.4 0.3 0.4 0.3 0.5 0.5 0.3 0.2

An Evolutionary Memetic Weighted … Table 5 Frequent single ruleset

195

Frequent single rules

Weighted support

Male ! yes

0.8

5 Experiment Result To evaluate the memetic algorithm-based weighted associative classification, the proposed algorithm has been tested with benchmark datasets from UCI repository [27]. Moreover, the proposed algorithm was compared with traditional well-known algorithms such as CBA, CMAR, and MCAR. All the experiments were tested on 3 GHz AMD Vision processor with 8 GB of physical memory. The java language was used for implementation purpose. A brief description about the datasets is given in Table 6. Holdout approach [47] was used where 90% of the datasets are utilized as training data and remaining 10% of the datasets are used as testing data. The system performance was examined by different metrics such as accuracy, precision and recall for heart disease and other datasets. The dynamic minimum threshold set for weighted support and confidence parameters was used for testing all the algorithms. Table 7 shows the accuracy for the EMWACA algorithm proposed in this chapter and the corresponding graphical representation shown in Fig. 2. Tables 8 and 9 represent the precision and recall of the proposed algorithm. Also, Figs. 3 and 4 shows the precision and recall results of our proposed algorithm on heart disease dataset. Table 10 illustrates the accuracy computation for various medical datasets and present in Fig. 5. Table 6 Dataset description

Dataset

No. of transaction

No. of classes

Heart disease Breast cancer Breast-w Diabetes

303 286 699 768

2 2 2 2

Table 7 Accuracy of all the algorithms on heart disease datasets

Algorithm

Accuracy

CBA CMAR MCAR EMWACA

87 88 91 95.4

Table 8 Precision of heart disease datasets

Algorithm

Precision

CBA CMAR MCAR EMWACA

83 82 85 92

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Table 9 Recall of heart disease datasets

Algorithm

Recall

CBA CMAR MCAR EMWACA

71 74 73 89

Table 10 Accuracy of medical datasets

Algorithms

CBA

CMAR

MCAR

EMWACA

Heart disease Breast cancer Breast-w Diabetes

87 78 80 76

88 84 79 88

91 67 75 88

95 98 88 94

Fig. 2 Accuracy of heart disease datasets

Fig. 3 Precision of heart disease datasets

100 95 90 85 80

95

Accuracy Accuracy

Precision

90 85 80

Precision

75

Fig. 4 Recall of heart disease datasets

100 80 60 40 20 0

Recall

Recall

An Evolutionary Memetic Weighted … Fig. 5 Accuracy of medical datasets by different classification algorithms

197 100 80 60 40 20 0

Heart Disease Breast Cancer Breast-w Diabetes

6 Conclusion and Future Work In this chapter, we have presented a new weight-based AC based on memetic algorithm. The hybrid search aimed to enhance the accuracy of AC by applied random walk memetic algorithm. At first, the network graph was drawn followed by weight computation derived from random walker. Based on these weights, CAR is defined for heart disease prediction. Usually, the weight of the itemsets was derived either from the profit or it would be assigned by the subject experts externally. This kind of assignment is not feasible for domains like click stream and network traffic analysis. As future research direction, the proposed memetic algorithm can be applied to other real-time domains, which require higher accuracy. Moreover, we plan to manipulate the use of rule ranking, weight, pruning in the proposed work and record the result.

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