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Abdulkadir Sengur

October 2009 The Arabian Journal for Science and Engineering, Volume 34, Number 2B

491

COLOR TEXTURE CLASSIFICATION USING WAVELET

TRANSFORM AND NEURAL NETWORK ENSEMBLES

Abdulkadir Sengur

Firat University, Department of Electronics and Computer Science, 23119, Elazig, Turkey

ﺔـﺻﻼﺨﻟا:

ﺪﻘﻟ

ﺮﺼﺘﻘﺗ

ﻰﻠﻋ

مﺪﺨﺘﺳا

نﻵا ﻰﺘﺣ

ٌ ﺮﻴﺜآ ﻲ?ﻓ تﺎ?ﺠﻳﻮﻤﻟا ﺔ?ﻴﻨﻘﺗ ﻦﻴﺜﺣﺎ?ﺒﻟا ﻦ?ﻣ

ﻂﻘﻓ ﺔﻳدﺎﻣﺮﻟا رﻮﺼﻟا .

ﺔﻴﺒﺼﻌﻟا تﺎﻜﺒﺸﻟاو تﺎﺠﻳﻮﻤﻟا ماﺪﺨﺘﺳا.

ﻒﻴﻨﺼ?ﺗ رﻮﺼ?ﻟا ﻲ?ﻓ ﺔﺠﺴ?ﻧﻷا ﺔﻌﺠﺸ?ﻣ ﺞﺋﺎ?ﺘﻧ ﻊ?ﻣ

ﻲ?ﻓ ةﺪ?ﻳﺪﺟ ﺔ?ﻘﻳﺮﻃ حﺮﺘﻘﻨﺴ?ﻓ ،ﺚ?ﺤﺒﻟا اﺬ?ه ﻲ?ﻓ ﺎﻣأ رﻮ?ﺻ ﻦ?ﻣ ﺔﺠﺴ?ﻧﻷا ﻒﻴﻨﺼ?ﺗ

. ﻞ?ﻌﻟو ﺐﻴﻟﺎ?ﺳﻷا ﺮ?ﺜآأ ﻞ?ﻴﻠﺤﺗ ﻲ?ﻓ ً ﺎ?ﺣﺎﺠﻧ

. ﻟا ﺪ?ﻤﺘﻌﺗو ﺔ?ﻘﻳﺮﻄ

ﺔﺠﺴ?ﻧﻷا

ﻞﻴﻠﺤﺗ ﻰﻠﻋ ﺔ?ﻧﻮﻠﻣ

ﻮﻜﺘﺗ ن ةﺪﻳﺪﺠﻟا ﺔﻘﻳﺮﻄﻟا ﻦﻣ جاﺮﺨﺘﺳاﻲﺠﻳﻮﻤﻟا ﻞﻴﻠﺤﺘﻟا ﻦﻣ ﺔﺻﺎﺧ تﺎﻤﺳ ، ﻒﻴﻨﺼﺗ ﻢﺛ ﻩﺬ?ه

ﻲﺠﻳﻮ?ﻤﻟا. ﺖ?ﻳﺮﺟأ ﺪ?ﻗو ﻊ?ﻣ ﺔ?ﻔﻠﺘﺨﻣ برﺎ?ﺠﺗ

تزوﺎﺠﺗ ﻒﻴﻨﺼﺘﻟا ﺔﻗد 98.%

ﺔﻴﺒﺼ?ﻌﻟا تﺎﻜﺒﺸ?ﻟا ماﺪﺨﺘ?ﺳﺎﺑ تﺎﻤﺴ?ﻟا

ةدﺪ?ﻌﺘﻣ تﺎﺤ?ﺷﺮﻣ

. ﺪ?ﻳﺪﺤﺘﻟا ﻞﻴﺒ?ﺳ ﻰ?ﻠﻋ و

ﺔ?ﻴﻟﺎﻌﻓ زاﺮ?ﺑﻹ تﺎ?ﺠﻳﻮﻤﻠﻟ ﻞ?ﻴﻠﺤﺘﻟا ﻦ?ﻣ ﺎ?ﻴﺑوﺮﺗﻷاو ﺔ?ﻗﺎﻄﻟا ﻞ?ﺜﻤﺗ ﻲﺘﻟا تﺎﻤﺴﻟا ﻞﻤﻌﺘﺴﻧ فﻮﺴﻓ

ﺚﻴﺣ ﺔﻘﻳﺮﻄﻟا ﺔﻧﺎﺘﻣ ﺞﺋﺎﺘﻨﻟا تﺮﻬﻇأونإ

ﺔﺣﺮﺘﻘﻤﻟا ﺔﻴﻨﻘﺘﻟا،

Corresponding Author:

E-mail: ksengur@firat.edu.tr

Paper Received September 24, 2008; Paper Revised December 27, 2008; Paper Accepted February 9, 2009

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ABSTRACT

The wavelet domain features have been intensively used for texture classification and texture segmentation with

encouraging results. More of the proposed multi-resolution texture analysis methods are quite successful, but all the

applications of the texture analysis so far are limited to gray scale images. This paper investigates the usage of

wavelet transform and neural network ensembles for color texture classification problem. The proposed scheme is

composed of a wavelet domain feature extractor and ensembles of neural networks classifier. Entropy and energy

features are integrated to the wavelet domain feature extractor. Various experiments have been carried out with

different wavelet filters. The performed experimental studies show the efficacy of the proposed structure for color

texture classification. The highest success rate is over 98%. Moreover, we compare our results with wavelet energy

correlation signatures [2].

Key words: wavelet decomposition, neural network ensembles, texture classification, feature extraction, entropy,

energy correlation

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493

COLOR TEXTURE CLASSIFICATION USING WAVELET TRANSFORM AND

NEURAL NETWORK ENSEMBLES

1. INTRODUCTION

Texture analysis plays an important role in many image processing tasks, ranging from remote sensing to medical

image processing, computer vision applications, and natural scenes. A number of texture analysis methods have been

proposed in the past decades (e.g., [1]) but most of them use gray scale images, which represent the amount of

visible light at the pixel’s position, while ignoring the color information. The performance of such methods can be

improved by adding the color information because, besides texture, color is the most important property, especially

when dealing with real world images [2].

Texture can be defined as a local statistical pattern of texture primitives in an observer’s domain of interest.

Texture classification aims to assign texture labels to unknown textures, according to training samples and

classification rules. Two major issues are critical for texture classification: the texture classification algorithms and

texture feature extraction. The main aim of texture classification is to find a best matched category for a given

texture among existing textures. Texture has been analyzed extensively and many texture recognition schemes have

been proposed [2-4]. The important property all have in common is that they constitute an appropriate model for

relationships between the adjacent pixels of a neighborhood.

A number of researchers have proposed algorithms for texture analysis, but all the applications of the texture

analysis so far are limited to gray scale images [5]. Recently, however, both color and texture information have been

used by several researchers [2,6]. A method has been proposed by Caelli and Reye [7]. They extract features from

three spectral channels by using three multi-scale isotropic filters. Van de Wouver et al. [2] proposed wavelet

energy-correlation signatures and derive the transformation of these signatures upon linear color space

transformation. The wavelet domain based co-occurrence matrix method and the second order statistical features

were used for color texture classification in the study of Arivazhagan et al. [8]. A set of features are derived and

color texture classification is done for different combinations of the features and for different color models. Karkanis

et al. [9] proposed a new approach for the detection of tumors in colonoscopic video. The proposed algorithm is

based on a color feature extraction scheme to represent the different regions in the frame sequence. The scheme is

built on the wavelet transform. The features called color wavelet covariance are based on the covariance of second

order textural measurement. A linear discriminant analysis is used for classification of the image regions. Sengur

proposed wavelet transform and ANFIS for color textures classification [6]. Crouse et al. has proposed a framework

for statistical signal modeling based on the wavelet domain hidden Markov tree [10]. The algorithm provided an

efficient approach to modeling of wavelet coefficients that are often found in real world images. Xu et al., have

shown that the wavelet coefficients have certain inter-dependences between color planes [11]. They used wavelet

domain hidden Markov model for color texture analysis. The proposed approach is used for modeling the

dependences between color planes as well as the interactions across scales. The wavelet coefficients at the same

location scale and sub-band, but with different color planes, are grouped into one vector and a multivariate Gaussian

mixture model is employed for approximating the marginal distribution of the wavelet coefficient vectors in one

scale.

In this paper, a color texture image classification scheme is proposed which uses wavelet transform and

ensembles of neural networks. Feature extraction in the wavelet domain and classification with ensembles of neural

networks are proposed. Wavelet entropies and wavelet energies of each color plane at different scales are used for

forming the feature vectors. An ensembles-based method enables an increase in generalization performance by

combining several individual neural network trains on the same task. The experimental studies show the efficiency

of the proposed system. Moreover, a comparison of the proposed schema with the wavelet energy correlation

signatures is conducted.

The organization of this paper is as follows: in Section 2, we summarize the theory for wavelet transform, neural

networks, the ensembles-based method, and the extraction of the energy correlation signatures; several brief

definitions are given in this section; in Section 3, the methodology and the implementation of the proposed process is

given; in Section 4, an experimental study is introduced and the classification results are shown; in Section 5, we

finally conclude the study.

2. BACKGROUND

In this section, the theoretical foundations for the expert system used in the present study are given in the

following subsections.

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2.1. Discrete Wavelet Transform

Wavelet transforms are finding intense use in fields as diverse as telecommunications and biology. Because of

their suitability for analyzing non-stationary signals, they have become a powerful alternative to Fourier methods in

many medical applications, where such signals abound [12]. The main advantage of wavelets is that they have a

varying window size, being wide for slow frequencies and narrow for the fast ones, thus leading to an optimal time-

frequency resolution in all the frequency ranges. Furthermore, owing to the fact that windows are adapted to the

transients of each scale, wavelets are not stationary.

The (continuous) wavelet transform of a 1-D signal f (x) is defined as:

*

( , )

a b

1/2

−

( , )

a b

( W f)( , ) a b, ( , )a b

ψ

( )( ) x dx

()

f f x

xb

a

a

ψ

ψ

ψψ

+∞

∫

−∞

==

−

=

(1)

where a is the scaling factor, b is the translation parameter related to the location of the window, and ψ*(x) is the

transforming function. The extension to 2-D is usually performed by using a product of 1-D filters. The transform is

computed by applying a filter bank to the image. The rows and columns of an image are processed separately and

down sampled by a factor of 2 in each direction, resulting in one low pass image LL and three detail images HL, LH,

and HH. Figure 1(a) shows the one-level decomposition. The LH channel contains image information of low

horizontal frequency and high vertical frequency, the HL channel contains high horizontal frequency and low

vertical frequency, and the HH channel contains high horizontal and high vertical frequencies. The frequency

decomposition is shown in Figure 1(b). Note that in multi-scale wavelet decomposition, only the LL sub band is

successively decomposed.

LL1LH1

HL1 HH1

(a) 1-level decomposition.

HH2

LH1

HL1HH1

LH2

HL2

LL2

(b) 2-level decomposition.

Figure 1. Wavelet frequency decomposition

2.2. Artificial Neural Networks (ANN)

Artificial neural networks were originally developed by researchers who were trying to mimic the

neurophysiology of the human brain [13]. By combining many simple computing elements (neurons or units) into a

highly interconnected system, a complex phenomenon such as intelligence is produced. A schematic diagram for an

artificial neuron model is shown in Figure 2. Nowadays, neural network researchers have incorporated methods from

statistics and numerical analysis into their networks. More specifically, feed-forward neural networks are a class of

flexible non-linear regression, discriminant, and data reduction models. By detecting complex non-linear

relationships in data, neural networks can help to make predictions about real-world problems.

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495

f(.) a(.)

x1

x2

xm

wi2

wi1

wim

weights

θi

outputs

yi

bias

inputs

Figure 2. Artificial neuron model [14]

The neural network node provides a variety of feed-forward networks that are commonly called back-

propagation networks. Back-propagation refers to the method for computing the error gradient for a feed-forward

network, a straightforward application of the chain rule of elementary calculus [14,15]. By extension, back-

propagation refers to various training methods that use back-propagation to compute the gradient. By further

extension, a back-propagation network is a feed-forward network trained by any of various gradient-descent

techniques. There are numerous algorithms available for training neural network models; most of them can be

viewed as a straightforward application of optimization theory and statistical estimation. Most of the algorithms used

in training artificial neural networks are employing some form of gradient descent. This is done by simply taking the

derivative of the cost function with respect to the network parameters and then changing those parameters in a

gradient-related direction. The most popular of them is the back propagation algorithm, which has different variants.

Standard back propagation is a gradient descent algorithm. It is very difficult to know which training algorithm will

be the fastest for a given problem, and the best one is usually chosen by trial and error. An ANN with a back

propagation algorithm learns by changing the connection weights, and these changes are stored as knowledge.

Neural network’s are trained by experience; when an unknown input is applied to the network it can generalize

from past experiences and product a new result. The output of the neuron net is given by Equation 2:

⎛

=+

∑

=

1j

⎟⎟

⎠

⎞

⎜⎜

⎝

−

m

ij ij

θ

(t)xwa 1)y(t

∑

=

j

−=∆

m

ij ijii

xw netf

1

θ

(2)

where X=(X1, X2… Xm ) represents the m inputs applied to the neuron, Wi represents the weights for input Xi, θi is a

bias value, and a(.) is an activation function. There are many kinds of activation function. Usually, non-linear

activation functions such as sigmoid or step are used.

2.3. Neural Network Ensembles (NN-ensembles)

An ensemble of classifiers is a collection of several classifiers whose individual decisions are combined in some

way to classify the test examples [16]. It is known that an ensemble often shows much better performance than the

individual classifiers that make it up. Assume that there is an ensemble of n classifiers: {

test data x. If all the classifiers are identical, they are wrong at the same data, where an ensemble will show the same

performance as individual classifiers. However, if classifiers are different and their errors are uncorrelated, then

when

( )x is wrong, most of other classifiers except for

}

12

,,...,

n

f ff

and consider a

if ( )x may be correct. Then, the result of majority voting

1

2

nn

k n k

pp

==

if

can be correct. More precisely, if the error of individual classifier is

p <

and the errors are independent, then the

probability

E

p that the result of majority voting is incorrect is

()

/ 2/2

1

2

(1)(( ) )

k

knkn

−

−<

∑∑

. When the size of

classifier n is large, the probability

E

p becomes very small.

Several methods for constructing an ensemble of classifiers have been developed in the last two decades. The

most important thing in constructing the NN ensemble is that each individual NN becomes different from the other

NNs as much as possible. This requirement can be met by using different training sets for different NNs. Some

methods for selecting the training samples are bagging, boosting, randomization, stacking, and dagging [16–8].

Among them, we put focus on bagging.

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2.3.1. Bagging

In bagging, each classifier is calibrated on a randomly drawn training set with the probability of drawing any

given example being equal [18]. Samples are drawn with replacement, so that some examples may be selected

multiple times while others may not be selected at all. As a result, each classifier could return a higher test set error

than a classifier using all of the data. However, when these classifiers are combined by voting, the resulting

ensemble produces lower test set error than a single classifier. The diversity among individual classifiers

compensates for the increase in error rate of any individual classifier and improves prediction performance.

2.4. Energy Correlation Signatures

Van de Wouver et al. [2] defined the wavelet energy correlation signatures as follows:

,

, , , ,

B j l i

, , ,

B j l i

00

1

jj

mnmn

N

∑∑

N

X

B j

XXX

il

jj

Cww

N x N

==

=

(3)

where

, , ,

B j l i

m

X

w

is the wavelet coefficient at (l, i) location at j scale in B (

{

B, j

C| m,n

energy signatures. They capture the energy distribution of the wavelet coefficients over the scale, sub band, and

color space for m = n and the others (m ≠ n) represent the covariance between different color spaces.

{

}

}

,,B LH HL HH

∈

) sub band and Xm is the

}

1,2,3,...,J

is called the wavelet

color space (m = 1,2,3). The set

{

mn

X ,X

1,2,3;m n;BLH,HL,HH ; j

=≤∈=

3. METHODOLOGY

The proposed color texture classification algorithm is illustrated in Figure 3. The steps involved in color texture

classification are as follows:

Step 1: The input to the NN-ensemble based color texture classification system is the color textures of size 512x512

[21]. We make sub images of size 128x128 by randomly choosing from the original input texture. Thus, color texture

sub images may overlap. For gray scale texture analysis, the color information is discharged by RGB to gray scale

transformation. Then the red, green, and blue components are decomposed from the color texture images and saved

for subsequent processing.

Step 2: This step involves both feature extraction and classification. The feature extraction is composed of two

layers. These are the wavelet decomposition layer and the entropy and energy calculation layer.

1. Wavelet decomposition layer: For wavelet decomposition of each of the red, green, and blue components of

color textures, the pyramid wavelet structure is used. We obtain one-level wavelet decomposition, and save

only the three detail images HH, LH, and HL where H and L stand for the high pass and low pass band in

each of the horizontal and vertical orientations for the subsequent calculation of entropy and energy

quantities.

2. Entropy and energy calculation layer: This layer is responsible for calculating the entropy and energy

quantities of each LH, HL, and HH of the red, green, and blue components of the color texture images. Thus,

entropy and the energy quantities are the features that characterize the color texture images.

3.1. Entropy

Entropy is a quantity that is widely used in information theory and is based on probability theory [15,18].

Entropy is a common concept in many fields, mainly in mechanics, image processing, and signal processing. The

general form of the entropy is given by

2

1

() log

n

ii

i

H Xpp

=

= −∑

(4)

where X is a random variable which can be one of the values

0

, then

2

0log 0 is defined as 0. Thus, H(X) can be interpreted as representing the amount of uncertainty that

exists in the value of X. In information theory, entropy value is considered to be an average amount of information

received when the value of X is observed. In this paper, we use the norm entropy. The norm entropy H is defined as

follows:

12

, ,...,

n

x xx

with probability

12

, ,...,

n

p pp . Note that

if

ip =

,, , ,

B l i j

00

mm

p

NN

X

B l

X

ij

Hw

==

=∑∑

, for ()

12p

≤<

(5)

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where

color space (m = 1,2,3) [20].

, , ,

B l i j

m

X

w

is the wavelet coefficient at (i ,j) location at l scale in B (

{}

,,B LH HL HH

∈

) sub band and Xm is the

3.2. Energy

Energy is commonly used for texture analysis. In this study, we use the averaged l2-norm, which is defined as

follows:

2

,, , ,

B l i j

00

1

*

()

mm

NN

X

B l

X

ij

Ew

NN

==

=

∑∑

(6)

where

color space (m = 1,2,3) [20].

, , ,

B l i j

m

X

w

is the wavelet coefficient at (i ,j) location at l scale in B (

{}

,,BLH HL HH

∈

) sub band and Xm is the

3.3. NN-Ensembles:

The ensemble size is taken as 25, since it has been shown that for many ensemble problems, the biggest profit in

accuracy is already made with this number of individual classifiers [18]. A standard three-layered back-propagation

network with the tangent- sigmoid transfer function is considered. The weights and biases of the neural networks are

initialized randomly and the number of neurons in the hidden node is determined heuristically as inputs

small value of the learning rate (0.15) and a large value of the momentum rate (0.8) are chosen to avoid local

minima. The number of training epochs was 500.

outputs

+

. A

Color texture image

(512x512)

Sub image

(128x128)

Sub image

(128x128)

Randomly selected sub image

(128x128)

RBG

LLLH

HLHH

LLLH

HL HH

LL LH

HLHH

Feature Extraction

Wavelet Entropy

Wavelet Energy

Neural Network Ensembles

Decision

Figure 3. The proposed color texture classification scheme

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4. EXPERIMENTS AND DISCUSSIONS

16 real world RGB color images of size 512 x 512 from different natural scenes are used in the experimental

studies. Figure 4 shows the color texture images. A data base of 1920 color image regions of 16 texture classes of

size 128 x128 was constructed randomly by subdividing each color texture image. 320 of the color texture sub

images were used for training the NN-ensembles. This means that 20 sample sub images for each texture class are

used for training. 100 sub images for each texture class were constructed for testing. This also means that 1600 sub

images in total were used for testing. For comparison purposes, four different randomly chosen wavelet filters are

used. These filters are Bior 4.4, coif4, sym4, and db7. The following feature vectors were constructed:

(1) Intensity (gray scale) images were obtained from the RGB form, thereby discarding the color information.

The features are extracted according to the step 2 of Section 3. Thus, 6 features are obtained for gray scale

textures (1 gray scale image x 3 wavelet detail images x 2 features (norm entropy and l2 norm energy)).

(2) Each R, G, and B component was wavelet transformed using one-level decomposition and the proposed

feature extraction scheme was employed. This process constructed a feature vector of size 18 (3 color

channels x 3 wavelet detail images x 2 feature values (norm entropy and l2 norm energy)).

(3) For performance comparison of the proposed method with the energy correlation signatures, which was

explained in Section 2.4. Each R, G, and B color component was wavelet transformed using one-level

decomposition, and wavelet energy correlation signatures were calculated. This process constructed 18

features.

The experiments are conducted based on the methodology which is illustrated in Figure 3 and the experimental

results are presented at Table 1. The correct classification rates are indicated for all color texture types and the

related wavelet filters. The p parameter which is used for norm entropy function is 1,5. This value is chosen after

many trials. Table 1 indicates the experimental results for gray scale texture images. Flowers1, Clouds, and Misc

texture images are correctly classified for all wavelet filter types. The correct classification rate is 100 % for these

texture images. This high correct classification rate is obtained for these color texture types because of their

homogeneity. On the other hand, the high correct classification rates are not obtained for the rest of the gray scale

texture images. Another important property which can be extracted from the results is that the Bior4.4 wavelet type

is produced much more accurately than the other wavelet filter types. The correct classification rates for each

wavelet filter type of the gray scale texture images varied between 85.4% and 93.2%.

One observation from experimental results is that features and the subsequent classification performance are

significantly improved when color information is added to the texture property. For example, for Bior4.4 wavelet

filter type, the correct classification rate is 93.2% for feature vector – 1 (gray level texture features). This correct

classification rate is increased to 98.9% when the color information is used (feature vector – 2). The correct

classification rates for color texture images are also given in Table 1. The correct classification rates are nearly 100

% for all wavelet filter types. 100 % correct classification rate is obtained for Flowers1, Clouds, Misc, Fabric2,

Fabric3, and Water texture images. Moreover, for Bior4.4, 13 color texture images are recognized with a success rate

of 100%. On the other hand, similar high correct classification rates are obtained with the other wavelet filters type.

Our goal is investigating the color texture classification improvement so we also compare our proposal with the

wavelet energy correlation signatures. As can be seen from Table 1, for the feature vector – 3, the overall correct

classification rate is higher then the feature vector – 1. Only two color texture images (Clouds and Misc) are

classified correctly as 100 % success rate. The correlation signatures have more successful results when Bior4.4 and

coif 4 wavelet types are used.

The proposed color texture classification scheme can be used for color texture segmentation, as we know that

color texture segmentation is more important for computer vision applications and content based image retrieval. For

instance, for segmentation of the color texture images, the wavelet entropy and wavelet energy values are computed

for a small window centered on each pixel of the image, resulting in one dimensional feature vector per pixel. Each

pixel is then assigned to a particular image region by neural network (supervised) or clustering techniques

(unsupervised).

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Figure 4. Color texture images from left to right and top to bottom: Grass, Flowers1, Flowers2, Bark1, Clouds, Fabric7, Leaves,

Metal, Misc, Tile, Bark2, Fabric2, Fabric3, Food1, Water, and Food2

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Table 1. Results of Texture Classification Using NN-Ensembles With Different Wavelet Filters

(for 1920 Image Regions)

Correct classification (%)

Wavelet Filter Types

Bior 4.4

Grass 90

Flowers1 100

Flowers2 87

Bark1 70

Clouds 100

Fabric7 100

Leaves 95

Metal 95

Misc 100

Tile 85

Bark2 84

Fabric2 97

Fabric3 100

Food1 100

Water 100

Food2 88

Grass 100

Flowers1 100

Flowers2 95

Bark1 100

Clouds 100

Fabric7 100

Leaves 100

Metal 94

Misc 100

Tile 100

Bark2 100

Fabric2 100

Fabric3 100

Food1 100

Water 100

Food2 93

Grass 100

Flowers1 82

Flowers2 96

Bark1 70

Clouds 100

Fabric7 91

Leaves 77

Metal 100

Misc 100

Tile 100

Bark2 100

Fabric2 100

Fabric3 100

Food1 100

Water 100

Food2 98

Overall results

Feature Vector – 1 93.2

Feature Vector – 2 98.9

Feature Vector – 3 94.6

Images

Coif 4

82

100

70

70

100

97

85

97

100

99

60

62

94

91

100

80

100

100

100

99

100

100

99

99

100

100

100

100

100

100

100

97

94

71

78

93

100

70

78

83

100

100

100

100

76

97

99

100

Sym 4

88

100

74

85

100

85

98

91

100

98

87

100

92

81

98

98

100

100

100

90

100

91

100

100

100

96

97

100

100

99

100

91

98

78

78

100

100

71

72

71

100

91

92

90

83

99

100

87

Db7

90

100

70

53

100

90

71

89

100

100

91

100

93

75

84

61

98

100

100

88

100

100

97

100

100

100

98

100

100

100

100

97

94

78

84

86

100

72

72

72

100

91

83

100

77

91

98

79

Feature Vector – 1

Feature Vector – 2

Feature Vector – 3

86.7

99.6

89.9

92.9

97.8

88.1

85.4

98.6

86.1

Total

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October 2009 The Arabian Journal for Science and Engineering, Volume 34, Number 2B

501

5. CONCLUSIONS

In this paper, we have discussed the effect of the color and wavelet domain features on the texture classification

problem. The main aim of the study is combining the color and texture information to improve the classification of

the texture images. We proposed a system which uses the wavelet domain entropy and energy quantities of the red,

green, and blue component of the RGB texture images. Among the three wavelet based methods that we examined,

our proposed system for color texture provides the best classification result.

Experimental studies and subsequent results using a set of real world colored texture images show the usefulness

of the wavelet entropy and energy for color texture analysis. The results show that color is an important component

for improving the classification results for the texture analysis problem.

In this study, several important parameters such as wavelet decomposition level, wavelet filter type, and norm

entropy parameter value are constant. Selecting the best decomposition level is an important issue. Furthermore,

selecting the best wavelet filter type and the best p parameter value for norm entropy will be studied in the future.

Another important point is the chosen color space. Several color spaces, such as K-L color space, I1I2I3 color space,

and UVW color space, will be added in future works.

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[13] C. M. Bishop, Neural Networks for Pattern Recognition. Oxford: Clarendon Press, 1996.

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[15] A. Sengur, I. Türkoğlu, and M. C. ve İnce, “Wavelet Oscillator Neural Networks for Texture Segmentation”, Neural

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[16] J. J. Rodríguez and J. Maudes, “Boosting Recombined Weak Classifiers”, Pat. Rec. Let., 29(2008), pp. 1049–1059.

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502

[19] F. Phan and E. Micheli-Tzanakou, Supervised and Unsupervised Pattern Recognition: Feature Extraction and

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