Machine Learning Techniques for Ontology-based Leaf Classification 0-7803-8653-1

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2004 8th International Conference on Control, Automation, Robotics and Vision Kunming, China, 6-9th December 2004

Machine Learning Techniques for Ontology-based Leaf Classiication Hong Fu., Zheru Chil*, Dagan Fengl,l, and Jiatao SonglJ

Center for Multimedia Signal Processing, Department of Electronic and In forma t ion

1

The Hong Kong Polytechnic Universit y, Hung Hom, Kowloon, Hong 2

3 Department

E ng ine ering

Kong

School ofInformation Technologies, The University of S ydney, NSW 2006, Australia

ofInformation and Communication En gineering , Zhejiang University, Hangzhou 310027,

P. R.

China

*Corresponding author: [email protected] Abstract

Leaf classiication,

indexing as well as retrieval is an

important part of a

s ystem. In

computerized

this p ap er,

an integrated approach for an system

ontology-based leaf classification

for the automatization classification, a

contour

proposed of

of the

measurement on For the

leaf vein by

extracted

thresholding

leaf

method

cmplo ying

and

neural

obt ain more vcin

details

the

an

vein

eicient

texturc

to

of a leaf.

Compared with

thc

Chrysanthemum leaves

through

(CCS). 1m ct al. [4]

used

the

shape

[2]

built

Point

models within the

a!. [3] introduced a classiication

Curvature

a

Systemic

by Wang et al. [5-7] since 2000. They adopted three inclu d ing Centroid-Contour

Histogram (ACH)

to represent a leaf shape and then

employed a fuzzy integral to fuse these three feature sets. The method

was

tested on a

database c ontaining 830

83 Chinese medicinal plants

l eaf images from

speciic

knowledgc

in the

Howevcr, no attempts

domain (ontology) of botany, and therefore provides a more

practical

system

for uscrs to comprehend and

handle. Primary experiments have shown promising results

ontology-based leaf classification,

the lea.

A

indexing

att rac tive. The system should

example".

For a given query leaf image, the

terminologi e s in the feature to

the query imag e.

plant species, the stud y on leaf image

In this paper, an

based

on

contour information

conducted for a computer-aided

plant

has

been

ontoloy

identiication

system [I]. In recent years, some research has

been done

used

including

le af ontology

leaf shape. Nielsen [2], Abbasi © 2004 IEEE

et al.

[3] and 681

the

is

by

p resented. The features to

database and

low

level

features

of

approach of ontology-based automatic

leaf classification

on recognizing plant species with plant componcnts

0-7803-8653-1/0/$20.00

top

matched leaf imag es will be retuned based on the

leaf is considered as an impor tant

characterize retrieval

transfer the

used by botanists to describe

very

and

support two kinds of retrievals, keywords and "query by

Introduction the

made to

taxonomy

an

Since

have been

system for

and retrieval is

and proven the fe asibility of the proposed

system.

1

Distance

(CCD), Moment Invariants (MIs). and Angle Code

encouraging results have been achieved.

the

to r ecogniz e

been carried out

past studies, the proposed method integrates low-level

image and

Space

investigations on

features of

an

Scale

of

hierarc hical polygon

rcpresentation of leaf shap e

shape featurc sets

is

as

of the

Abbasi et

the Acer family variety.

combined

network approach so

using

shape based leaf image retrieval have

The

thc method used in botany.

recognition,

by

Nielsen

semi- au tomat i c

for

a pproximation

trained neural network

an unlobed leaf is also conducted

automatically accordi n g to

leaves.

and investigated the variabili ty

type

is employed to re cognize the detailed tooth pattens.

classiication

same plant species.

taxonomy pri ncip le

the similar

ado pted by the botanists. Th en a

and

of plant

role

CCD code system is margin

recognition

Distribution Models (PDMs) for seven diferent plants

proposed,

system. For the

the basic shapc and

to categorize

' a leaf by using

scaled

is

playa crucial

obtained some p reliminary results on plant

features

plant identiication

whcrein machine Icaning techniques

1m et al. [4]

proposed .

botanists

method

to

Section

describe a

of mapping

linguistic tems in is

In

the

2, leaf

the is

the low-level shape bra�ch of

d iscussed in Section 3. Leaf vein

extraction

is

introduced

in

Section

conclusions are drawn in Section 5.

2

4.

Finally,

usually has a palmate vein patten. The textures of high-order veins are useul for recognition.

Ontology for Leaf Classiication

3

Shape-based Classiication of Leaves

A

leaf can

be

assigned

different

to

classes

and

subclasses according to the ontology shown in Fig. I. This time-consuming task is usually carried out by

human being s in the ield of botany. Automatic leaf

classification based on machine' leaning techniques releases the burden from botanists so that they can pay ��,

� � � W

more attention to higher level researches.

nt iy

3.1 First-Stage Classiication - Using the Scaled CCD Code to Recognize Lobation

Band C are independent

attibutes of A

and Tooth The Centroid-Contour Distance (CCD) curve

Band C are dependent

a

attibutes of A

popular

representation

set of dependent

one-dimensional curve by

two-dimensional

of

shape,

shape

is

In

is a

which

converted

to

a

calculating the distances

between the centroid and the pixels on the contour

attributes

a

[5].

The large scale variation (lobation) and small scale

Figure I. Ontology for a lea.

variaion (tooth) of a leaf arc both preserved in CCD, but it is hard to classify leaves by comparing their CCDs directly due to the lack of scaling and rotation invariant

An ontology deines a common vocabulary for people

who need to share information in a domain, which

properties. We proposed scaled CCD code which is

[8]. Since there is

scaling invariant and rotation invariant to characterize

no single well-accepted ontology-design methodology,

the leaf shape. The scaled CCD code is obtained by

enables reuse of domain knowledge

we built t he ontology to describe a leaf with emphasis

following four steps below:

on the taxonomy commonly used in botany [9, 10] and

Step I: Smooth CCD(x), x

propose automatic· mapping fom low level features to the semantic terms. As shown in Fig.

1,

Gaussion unction

ontology

shape branch, the lobation and the margin are wo independent and primary properties to characterize the

where

1, .. . ,N using no rmali zed

g(x) " -e 2-2

ceDis

consists of two main branches: shape and vein. In the

=

1

_[x_�)l 2,'

. The smoothed

MCCD(x) = pconv(CCD,g),x = 1,. . .N ,

pcon(

,

) is the periodic convolution.

Step 2: Segment the CCD using the smoothed CCD, i.e.,

shape of a leaf. That is to say, a leaf could be unlobed or

n;,

i

=

1, .. . , M , where MCCD crossing

lobed in spite of its margin or vice versa. The unlobed

to ind points

leaves are classiied according to their apex and base

CCD, as shown in Fig.2(d). Let the block signal

angles, while the lobed ones are classiied according to

BCCD(x) =

the number of lobations. The margin could be entire or . . toothed.

Furthermore,

the

toothed

margin

"

(1)

{maX(CCD), CCD(x) > MCCD(x); .

.

nin(CCD), CCD(x} < MCCD(x).

(2)

Step 3: Merge the segments according to diferent scales,

contains

dentate, crenate and serrate teeth. In the vein branch,

that is, let the depth of the gap

there are three basic pattens that are quite related to the basic shape. For example, a leaf with palmate lobation

682

p,

=

l

max[CCDcn, : nl+I)]-min[CCD(nH : n,)], f BCCD(n, : nl+]) :

max[CCDC "I_I

the local radius Ri

=

nJ]-min[CCD(n,

if G. ,

max(CCD); : nl+1

Step

mean(CCD(nH : ni+I)] ,

,

,

merge the two neighboring segments

:n;) and

CCD(n, :n;+I)'

4: Get a 7-bit scaled code by calculating the

number of segments ater each merging process, as

and the relative depth of the gap is then deined as

G=

1 D

CCD(nH

)], if BeCD(n, : n'+I)= min(CCD). "