texture image classification using support vector machine · texture image classification using...

Texture Image Classification Using Support Vector Machine

Mr.S.R.Suralkar Mr.A.H.Karode Ms.Priti W.Pawade

Asso.Prof. (E & TC Dept.) Asst. Prof. (E & TC Dept.) M.E. 2nd Year (Digital Electronics) SSBT’s COET, Bambhori, SSBT’s COET, Bambhori, SSBT’s COET, Bambhori,

Jalgaon. Jalgaon. Jalgaon.

[email protected] [email protected] priti_pawade6 @rediffmail.com

Abstract

Texture refers to properties that represent the

surface or structure of an object and is defined as

something consisting of mutually related elements.

The main focus in this study is to do texture

segmentation and classification for texture images.

Statistical features can be calculated based on the

grey level co-occurrence probabilities (GLCP)

generated. The statistical features used in this study

are uniformity, contrast, and entropy. The features

are obtained by using a combination of different

angles. For noise reduction, an appropriate moving

average is applied to the statistical features. To post-

process the image, support vector machines (SVM)

had been proposed to do classification on the

extracted features. Some kernel functions which are

being tested are second degree polynomial, radial

basis function (RBF), exponential radial basis

function (ERBF), sigmoid, and odd-order Bspline.

RBF and ERBF achieved the best classification

accuracy compare to other kernels used. SVM also

automatically helps RBF kernel to define the centres

during optimization. Brodatz texture album is used in

this study to test out the result. In the study, a

combined GLCP with SVM post-processing showed

a marked improvement over other classifier in terms

of classification accuracy.

Keywords: Support Vector Machines, Grey

Level Co-occurrence Probabilities, Image

segmentation, Texture Classification

1. INTRODUCTION Texture is defined as a pattern that is repeated

and is represented on the surface or structure of

an object. To separate textures into a single

texture type, first we need to preserve spatial

information for each texture. For instance, the

manual grey level thresholding which does not

provide the spatial information for each texture

that could generate in appropriate segmentation

result. Edge detection techniques used on

texture image could result in noisy and

discontinuous edges and therefore segmentation

process becomes more complicated Grey level

co-occurrence probabilities (GLCP) method is

used as a texture descriptor in the process of

feature extraction. The selection of certain

texture is possible as it is based on the

distribution in grey level co-occurrence matrix

(GLCM). Boundaries that separate between

textures can be created by searching the

gradients in one-dimensional (1D) GLCP

statistical features .The process of GLCP

extraction is arbitrary and takes unreliable time.

Some approaches had modified the structure of

GLCP algorithm in order to speed up the

computation time for texture feature extraction

process.

We present a novel texture classification

algorithm using Grey Level Co-occurrence

Probabilities (GLCP) method is being used to

extract features from texture image and support

vector machines (SVM)[4]. Grey Level Co-

occurrence Probabilities (GLCP) statistics are

used to preserve the spatial characteristics of a

texture. The selection of certain texture is

possible based on the statistical features[5]. The

best statistical features that are used for analysis

are entropy, contrast, homogeneity and

correlation. However, further analysis in shows

that correlation was not suitable for texture

segmentation. GLCP statistics can also be used

to discriminate between two different textures.

This feature vector is first used for classification

of the extracted features using the GSVM

(Gaussian SVM) classifier. The experimental

setup consists of images from the Brodatz

texture databases and a combination of some

images therein. The proposed method produces

promising classification results for both single

and multiple class texture analysis problems[ 4].

2. SVM – An Introductory Overview In the context of supervised classification,

machine learning and pattern recognition is the

extraction of regularity or some sort of structure

from a collection of

data. Neural networks (NN) and Bayesian

classifiers are the typical examples to learn such

organization from the given data observations.

Support Vector Machines (SVM) is a relatively

new classifier and is based on strong

foundations from the broad area of statistical

Priti W Pawade et al,Int.J.Comp.Tech.Appl,Vol 3 (1), 71-75

IJCTA | JAN-FEB 2012 Available [email protected]

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ISSN:2229-6093

mailto:[email protected]

learning theory [4]. Since its inception in early

90s, it has found applications in a wide range of

pattern recognition problems, to name a few:

handwritten character recognition, image

classification, financial time series prediction,

face detection, bioinformatics, biomedical

signal analysis, medical diagnostics, and data

mining.

SVM has become, in practice, the

classifier of choice of numerous researchers and

practitioners for several real-world classification

problems. This is because SVM is capable of

generalizing well (predicting the unseen or

unknown samples with a good degree of

accuracy) as compared to many traditional

classifiers (NN, etc.) It offers several

advantages which are typically not found in

other classifiers:

• Computationally much less intensive (esp. in

comparison to NN)

• Performs well in higher dimensional spaces

(a factor which limits many efficient

classifiers)

• Lack of training data is often not a severe

problem

• Based on minimizing an estimate of test error

rather than the training error (structural risk

minimization)

• Robust with noisy data (noise can severely

degrade the performance of NN)

• Does not suffer as much from the curse of

dimensionality and prevents overfitting

It is seen that support vector machine is a

powerfull classifier than other classifier.

2.1 Introduction to support vector machines

A binary class supervised classification problem

is usually formulated in the following way:

given n training samples (< xi >,yi) where

< xi > =(xi1,xi2,.....,xim) is an input feature vector

and yi ∈ {−1,+1} is the target label, the task of

the discriminant function or a classifier is to

learn the patterns in the training samples in such

a way that at a later stage it can predict reliably

a yi for an unknown xi . SVM is fundamentally

developed for such binary classification case

and is extendable for multi-class situation. Like

other linear classifiers, it attempts to evaluate a

linear decision boundary (assuming that the data

is linearly separable) or a linear hyperplane

between the 2-classes (Figure 1a). Theoretically,

when the data is linearly separable, there exist

possibly an infinite number of hyperplanes

(Figure 1b) which can correctly classify the

training data. SVM, unlike other classifiers of

its kind, strives to find out an optimal

hyperplane (Figure 1c). It is commonly believed

that points belonging to the two data classes

often lie in such a way that there is always some

„margin‟ between them. SVM attempts to

maximize this margin ( 2γ in Figure 1c) by

considering it as a quadratic programming

problem, see [4, 5] for mathematical

formulation and derivation of the solution.

3. METHODOLOGY

This paper considers the problem of

texture classification only for a gray-level case

which is conventionally tackled in two stages of

feature extraction and classification.

3.1 GLCP Feature Extraction:

GLCP is a discrete function that represents joint

probability, Cij, of different sets of pixels

having different grey levels, and is defined by

…………………(1)

where Fij is the co-occurrence matrix

constructed by the frequencies of two grey

levels of two relational pixels. G represents the

grey level quantization. The distance between

two relational pixels is set to become 1 for

micro-texture analysis. The common angle is

either 0°, 45°, 90° or 135°. To reduce the

computation time in GLCP feature extraction,

we set a window size, M×N or a block of pixels

as one feature value.

3.2. SVM Classification

The purpose of SVM is to map feature vectors

into a higher dimensional feature space, and

then creating a separating hyperplane with

maximum margin to group the GLCP features.

Support vectors (SVs) contain highlighted

pixels that help to create the margins or

boundaries in an image. The higher dimensional

space is defined by a kernel function. The kernel

functions that we used in texture discrimination

are shown in Table 1. For more detail on

learning kernels is described in Schölkopf

B.and Smola A. J. (2002).



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Type of

classifier

Inner Kernel Function

Polynomial

Radial basis

function

Tangent

hyperbolic

kernel

Table 3.1: Kernel functions for used in SVM

training

4. EXPERIMENTAL DESIGN

I. Test Images

Some of the Brodatz‟s textures (Brodatz, 1966)

had been used for our methods testing. The 8-

partite texture image having one sample of each

image with resolution of 989×98 pixels in

Figure 4.1 is created to measure GLCP

statistical approaches to identify textures.

II. Parameters Settings

We used an adequate grey level

quantization, G of 64 levels to construct GLCM

(Jobanputra & Clausi, 2006). The displacement

vector, (θ, d) is set to become (0, 1). The

window size configuration depends upon the

texture primitive size on the test image. The

bigger the window size we set, the more spatial

information we yield. However a window size

which is too large, may cause the overlapping

between textures at the boundaries. Given the

resolution of a test image, Ri, and the window

size, M×N. The resolution of the feature space

is defined by

…………………….(2)

To gain sufficient spatial information, we

recommended the Rf should have at least 50×50

pixels. Thus, from the equation (2), we obtain

the following equation defined by

………………………(3)

to adjustify our window size settings. The

bigger the resolution of feature space is

required, the smaller the window size that we

have to set. All the statistical features as shown

in Figure 4.1 to 4.4 Let Rf be equal to k×l,

where k and l is the number of rows and

columns of a feature space respectively. The

number of moving average, v must fulfill the

condition, v < k (4) if the window scanning

sequence starts from left to right and followed

by top to bottom or the condition v < l (5) if the

window scanning sequence starts from top to

bottom and followed by left to right.

Criteria Setting

Grey level quantization,G 64 levels

Distance,d 1 pixel

Angle, 00

Window size(MN) From equn 3

Satistical Feature Feature from

Table 3.2: GLCP parameters configuration

4. RESULTS :

Eight different kinds of textures with each

one sample have been chosen from the Brodatz

album (1966) to measure the GLCP statistical

features as stated in Hammouche et al. (2006)

[16]. The chosen textures have varying

characteristics in terms of the size of primitive

pattern, structure arrangements, brightness,

coarseness, and the statistical distribution

(Sonka et al., 2007). These textures are pressed

D1_01 and D1_02, D107_01 and D107_02,

D112_04 and D112_08, D98_03 and D98_04.

Fig 4.1 shows corresponding

statistical feature contrast of GLCP.it is seen

that same image with different intensity of grel

level shows different graph of

it.figure4.2,4.3,4.4 shows statistical faeture of

correlation,energy and homogeneity.

FIG.4.1: D1_01 And D1_02 shows their

correspnding graoh of GLCP contrast



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correspnding graoh of GLCP correlation


correspnding graoh of GLCP Energy


correspnding graoh of GLCP Homogeneity

Table 4.1: Measurments of each feature in test

images

In order to assess the performance of the

proposed approach, experiments with the

Brodatz database [ 16] were carried out. In the

experiments, each Brodatz texture constitutes a

separate class. Each texture have 640 x 640

pixels. The samples were separated in two

disjoint sets, one for training and the other for

testing the classifier

The evaluation is based by the accuracy (see

Equation 4) These measurements are estimated

in random partitions of the training and test sets.

This approach is compared with several

classifiers in Li et al [15].

Accuracy = 100%

....................(4)

Figure 4.5 summarizes the results of the

proposed method, along with the results [15]

for the single and fused SVM classifier, the

Bayes classifiers using Bayes distance and

Mahalanobis distance, and the LVQ

classifier. These measurements are estimated

with random partitions of the training and test

sets.

Features D1_01 D107_0

2

D112_0

8

D98_

03

Contrast 0.0410 0.1453 0.0655 0.0941

Correlation 0.7197 0.8482 0.5746 0.828

7

Energy 0.8216 0.4380 0.7940 0.409

7

Homogeneity 0.9795 0.9280 0.9672 0.953

0



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FIG.4.5: Accuracy of texture classification

5. CONCLUSIONS In this paper, an approach for texture-based

image classification using the gray-level co-

occurrence probability(GLCP) and Support

vector machine (SVM) methods is presented

To show the usefulness of the proposed

methodology, an application with a benchmark

data set was considered. The proposed approach

is evaluated in terms of accuracy. And it is

compared with several classifiers in Li et al

[15]. Figure 4.5 show the superiority of

GLCP+SVM over the single and fused S VM,

the Bayes classifiers using Bayes distance and

Mahalanobis distance, and the LVQ classifier.

6. REFERENCE:

1.TUCERYAN, M., and JAIN, A. K.: „Texture analysis‟,

Handbook Pattern Recognition and Computer

Vision, World Scientific, 1993. 2.Sklansky, J., “Image Segmentation and Feature

Extraction,” IEEE Transactions on Systems, Man, and

Cybernetics, SMC-8, pp. 237-247, 1978.

3. Haralick, R.M., “Statistical and Structural Approaches

to Texture,” Proceedings of the IEEE, 67, pp. 786-804,

1979.

4 .VAPNIK, V.: „The nature of statistical learning theory‟.

Springer Verlag, 1995.

5.Dr.P.V.Ingole,A.H.Karode,S.R.Suralkar “Textured and

Non-textured image classification using wavelet transform for CBIR” National Conference on Emerging Trends in

Electronics Engineering & Computing Nagpur, Feb. 2010.

6. Bernd Heisele (2005), “Hierarchical classification and

feature reduction for fast face detection”. Handbook of

Pattern Recognition and Computer Vision (CH Chen, PSP

Wang, ed.), p.481-495. World Scientific Publ.

7.Rajpoot, K.M.; Rajpoot,.“Wavelets and support vector

machines for texture classification”N.M., Multitopic

Conference, 2004, Proceedings of INMIC 2004, 8th International, pp. 328 – 333.

8. Kwang In Kim, Keechul Jung, and Jin Hyung Kim,

“Texture-Based Approach for Text Detection in Images

Using Support Vector Machines and Continuously

Adaptive Mean Shift Algorithm IEEE Transactions On Pattern Analysis AndMachine Intelligence.Vol.25,No.12,

December 2003.

9. Lalit Gupta, Sukhendu Das, Shivani G. Rao;

"Classification of Textures in SAR Images using multi-channel multi-resolution filters"; NCIP-2005, March-2005,

NIAS IISc. Bangalore, India, pp. 198-201.

10. Hong-ChoonOng, Hee-KooiKhoo,“Improved Image Texture Classification Using Grey Level Co-occurrence

Probabilities with Support Vector Machines Post-

Processing”, European Journal of Scientific Research

ISSN 1450-216X Vol.36 No.1 (2009), pp.56-64.

11. Hee-KooiKhoo, Hong-ChoonOng, Ya-Ping

Wong,“Image Texture Classification using Combined

Grey Level Co-occurrence Probabilities and Support Vector Machines”, 2008 Fifth International Conference on

Computer Graphics, Imaging and Visualisation.

12. Shu-yi Zhang , Xiaorong Xue,Xi Zhang Kwang In Kim, Keechul Jung, Se Hyun Park, andHang Joon Kim

“Support Vector Machines for Texture Classification,

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AnalysisAndMachineIntelligence.Vol.24,No.11,November 2002.

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Transform and Support Vector Machines”, vol. 6, pp.3795

- 3798,IEEE International Geoscience and Remote Sensing Symposium, 2005.

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SVM-based edge detection method”, Pattern Recognition Letters 25, p.1143-1154, 2004.

15. S. Li, 1. T. Kwok, H. Zhu, and Y. Wang, " Texture

classification using the support vector machines," Pattern Recognition, vol. 36, no. 12, pp. 2883-2893, 2003.

16. P. Brodatz, Textures: A P hotographic Album for

Artists and Designers. Dover Publications, 1966



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