Wavelet Based Histogram Method for Classification of Textu
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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 6375(Online) Volume 4, Issue 3, May June (2013), IAEME
149
WAVELET BASED HISTOGRAM METHOD FOR CLASSIFICATION
OF TEXTURES
Jangala. Sasi Kiran1
,
U Ravi Babu2
, Dr. V. Vijaya Kumar3
1
(Research Scholar, University of Mysore, Mysore, Associate Professor & HOD-CSE,
VVIT, Hyderabad, A.P, India)2
(Research Scholar, Aacharya Nagarjuna University Asst. Professor, GIET Rajahmundry,
A.P, India)3
(Professor & Dean Computer Sciences, Anurag Group of Institutions, JNTUH, Hyderabad,
A.P, India)
ABSTRACT
To achieve high accuracy in classification the present paper proposes a new method
on texton pattern detection based on wavelets. Each texture analysis method depends upon
how the selected texture features characterizes image. Whenever a new texture feature is
derived it is tested whether it precisely classifies the textures. Here not only the texture
features are important but also the way in which they are applied is also important and
significant for a crucial, precise and accurate texture classification and analysis. That is the
reason the present paper applied the derived a new method called Wavelet based Histogram
on Texton Patterns (WHTP). So far no exhaustive work was carried out in the wavelet
domain for classification of textures, based on histogram of texton pattern extraction. This is
the principal motivation for the work done in this paper. The proposed WHTP method is
tested on stone textures for precise classification.The proposed texton pattern detection
evaluates the relationship between the values of neighboring pixels in the wavelet domain.The experimental results on various stone textures indicate the efficacy of the proposed
method when compared to other methods.
Key words: Texton, Pattern detection, neighboring pixels, feature extraction,stone textures,
multi resolution
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
& TECHNOLOGY (IJCET)ISSN 0976 6367(Print)ISSN 0976 6375(Online)
Volume 4, Issue 3, May-June (2013), pp. 149-164 IAEME:www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com
IJCET
I A E M E
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I. INTRODUCTIONTexture analysis plays an important role in many image processing tasks, ranging
from remote sensing to medical image processing, computer vision applications, and naturalscenes. A number of texture analysis methods have been proposed in the past decades [1, 2,
3, 4, 5, 6, 7] but most of them use gray scale images, which represent the amount of visible
light at the pixels 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 [8]. In contrast
to intensity, coded as scalar gray values, color is a vectorial feature assigned to each pixel in a
color image. Although the use of color for texture image analysis is shown to be
advantageous, the integration of color and image is still exceptional.
The wavelet methods [3, 4, 8, 9] offer computational advantages over other methods
for texture classification and segmentation. Study of patterns on textures is recognized as an
important step in characterization and classification of texture. Various approaches are
existing to investigate the textural and spatial structural characteristics of image data,including measures of texture [10], Fourier analysis [11, 12], fractal dimension [13],
variograms [14, 15, 16, 17] and local variance measures [18]. Fourier analysis is found as the
most useful when dealing with regular patterns within image data. It has been used to filter
out speckle in radar data [19] and to remove the effects of regular agricultural patterns in
image data [19]. Study of regular patterns based on fundamentals of local variance was
carried out recently [20, 21]. Hence, the study of patterns still plays a significant area of
research in classification, recognition and characterization of textures [22].
A wavelet transform-based texture classification algorithm has several important
characteristics: (1) The wavelet transform is able to decorrelate the data and achieve the same
goal as the linear transformation [23]. (2) The wavelet transform provides orientation
sensitive information which is essential in texture analysis. (3) The computational complexity
is significantly reduced by considering the wavelet decomposition. This is the reason theproposed WHTP employed wavelet transforms.
In [24] proposed a complex texton, complex response 8 (CR8) are used and an 8-
dimensional feature is extracted. After that, similar to MR8 [25], a complex texton library is
built from a training set by k-means clustering algorithm and then an texton distribution is
computed for a given texture image. The main drawback of this is, it lacks spatial
information. Texture patterns can provide significant and abundance of texture and shape
information. One of the features proposed by Julesz [26, 27] called texton, represents the
various patterns of image which is useful in texture analysis. In the present paper, Textons are
detected on wavelet decomposed texture image for texture classification. The different
textons may form various image features.
The proposed WHTP method is an extension of our earlier method [28], with multi
resolution and robust features. The proposed WHTP method attempted to classify various
HSV-based color stone textures classification based on frequency occurrence of textons in
wavelet decomposed image, which is different from the earlier studies. In this work,
classification accuracy can refer to the percentage of correctly classified texture samples.
The rest of the paper is organized as follows. Section 2 describes wavelet based texton
feature evaluation method. Experimental results and comparison the results with other
methods are discussed in section 3 and conclusions are given in section 4.
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2-level
DWT
Texton Frequency
Extraction
Feature
Library
II. COMPUTATION OF WAVELET BASED HISTOGRAMS ON TEXTONPATTERNS (WHTP)
The proposed wavelet based texton feature evaluation method is represented in thefollowing Figure 1.
Figure 1: Block diagram of (WHTP) Wavelet based Histograms onTexton Patterns
In this paper, the DWT is applied on a set of texture images and texton frequencies
are extracted from the approximation and detail subbands of DWT decomposed images, at
different scales. The various combinations of the texton frequencies are applied for texture
classification and a set of best feature vector are chosen. In order to improve the success rate
of classification, the texton frequencies are calculated for original image, approximation and
detail sub-bands of 1-level DWT decomposed images. It is found that the success rate is
improved much by combining the texton frequencies of original and decomposed images.
2.1 Discrete wavelet transformThe word wavelet is due to Morlet and Grossmann in the early 1980s. They used the
French word ondelette, meaning small wave. Soon it was transferred to English by
translating onde into wave, giving wavelet.
Today wavelets play a significant role in Astronomy, Acoustics, Nuclear Engineering,
Subband Coding, Signal and Image Processing, Neurophysiology, Music, Magnetic
Resonance Imaging, Speech Discrimination, Optics, Turbulence, Earthquake Prediction,
Radar, Computer and Human Vision, Data Mining and Pure Mathematics Applications such
as Solving Partial Differential Equations etc.
The most commonly used transforms are the Discrete Cosine Transform (DCT), Discrete
Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Discrete Laguerre Transform
(DLT) and the Discrete Hadamard Transform (DHT). The DCT is favoured in the early
image and video processing. There are large numbers of image processing algorithms that use
DCT routines. DCT based image processing techniques are robust compared to spatial
domain techniques. The DCT algorithms are robust against simple image processing
operations like low pass filtering, brightness and contrast adjustment, blurring etc. However,
they are difficult to implement and are computationally more expensive. DCT is one of the
most popular and widely used compression methods. The quality of the reconstructed images
in DCT is degraded by the false contouring effect for specific images having gradually
Original Texture
Images
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shaded areas. The false contouring occurs in DCT when smoothly graded area of an image is
distorted by an aberration due to heavy quantization of the transform coefficients. The effect
looks like a contour map. Due to this reason, the DCT based image processing
methods are weak against geometric attacks like rotation, scaling, cropping etc.To overcome the above drawbacks, the present paper adopted DWT techniques to
achieve better performance. The Discrete wavelet transform (DWT) is a powerful tool of
signal and image processing that have been successfully used in many scientific fields such as
signal processing, image compression, image segmentation, computer graphics, and pattern
recognition .
The DWT based algorithms, has been emerged as another efficient tool for image
processing, mainly due to its ability to display image at different resolutions and to achieve
higher compression ratio. In DWT, signal energy concentrates to specific wavelet
coefficients. This characteristic feature is useful for multi-resolution analysis. DWT provides
sufficient information both for analysis and synthesis of the original signal, with a significant
reduction in the computation time.
Haar wavelet is one of the oldest and simplest wavelet. Therefore, any discussion of waveletsstarts with the Haar wavelet. The Haar, Daubechies, Symlets and Coiflets are compactly
supported orthogonal wavelets. These wavelets along with Meyer wavelets are capable of
perfect reconstruction. The Meyer, Morlet and Mexican Hat wavelets are symmetric in shape.
The wavelets are chosen based on their shape and their ability to analyze the signal in a
particular application.
2.1.1 Salient features of Haar wavelet transform
The Haar wavelet is the first known wavelet. The Haar wavelet transform has a
number of advantages:
1. It is conceptually simple.2. It is fast.3. It is memory efficient, since it can be calculated in place without a temporary array.4. It is exactly reversible without the edge effects that are a problem with other wavelet
transforms.
The image is actually decomposed i.e., divided into four sub-bands and sub-
sampled by applying DWT as shown in Figure 2(a). These subbands are labeled LH1, HL1
and HH1 represent the finest scale wavelet coefficients i.e., detail images while the sub-band
LL1corresponds to coarse level coefficients i.e., approximation image. To obtain the next
coarse level of wavelet coefficients, the sub-band LL1 alone is further decomposed and
critically sampled. This results in two-level wavelet decomposition as shown in Figure 2(b).
Similarly, to obtain further decomposition, LL2 will be used. This process continues until
some final scale is reached. The values in approximation and detail images (sub-band images)
are the essential features, which are shown here as useful for texture analysis and
discrimination. In this paper/thesis Haar wavelet, Daubechies wavelets, and Symlet wavelet
are used for decomposition.
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(2a) (2b)
Figure 2: DWT Decomposition:
2(a) First level of DWT 2(b) second level of DWT
2.2 Texton detectionTextons [26, 27] are considered as texture primitives, which are located with certain
placement rules. A close relationship can be obtained with image features such as shape,
pattern, local distribution orientation, spatial distribution, etc.., using textons. The textons are
defined as a set of blobs or emergent patterns sharing a common property all over the image
[26, 27]. The different textons may form various image features. To have a precise and
accurate texture classification, the present study strongly believes that one need to consider
all different textons. That is the reason the present study considered all. There are several
issues related with i) texton size ii) tonal difference between the size of neighbouring pixels
iii) texton categories iv) expansion of textons in one orientation v) elongated elements of
textons with jittered in orientation . By this some times a fine or coarse or an obvious shape
may results or a pre-attentive discrimination is reduced or texton gradients at the texture
boundaries may be increased. To address this, the present paper utilized six texton types on a22 grid as shown in Figure 3(a). In Figure 3(a), the four pixels of a 22 grid are denoted as
V1, V2, V3 and V4. If two pixels are highlighted in gray color of same value in subband image
then the grid will form a texton. The six texton types denoted as TP 1, TP2, TP3, TP4, TP5 and
TP6 are shown in Figure 3(b) to 3(g).
V1 V2
V3 V4
(a) (b) (c) (d)
(e) (f) (g)
Figure 3: Six special types of Textons:
a) 22 grid b) TP1 c) TP2 d) TP3 e) TP4 f) TP5 and g) TP6b)
The working mechanism of texton detection for the proposed method is illustrated in Figure
4. The present paper conducted experiments using Harr wavelet transform due to its
advantage as specified in the section 2.1.1. First, the original image is decomposed using
LL1 HL1
LH1 HH1
LL2 HL2
HL1LH2 HH2
LH1 HH1
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Haar, transform. On the approximation subband image, textons are identified. Then
the frequency occurrences of all six different textons as shown in Figure 4, with different
orientations are evaluated. To have a precise and accurate texture classification, the present
study considered sum of the frequencies of occurrences of all six different textons as shownin Figure 3 on a 22 block.
TP2 TP1 0 3 0 0 2 2
3 1 0 0 0 3
TP3 0 0 3 4 0 0
0 0 3 2 0 0
TP4 TP4 TP3 5 0 4 3 1 0
1 5 2 4 1 5
(d) (e)
Figure 4: Illustration of the texton pattern detection process:
(a) 22 grid (b) wavelet transformed image (c) & (d) Texton location and texton types (e)
Texton image
III. RESULTS AND DISCUSSIONSExperiments are carried out on the proposed WHTP method to demonstrate the
effectiveness of the proposed method for stone texture classification. The proposed method
WHTP paper carried out the experiments on two Datasets. The Dataset-1 consists of various
brick, granite, and marble and mosaic stone textures with resolution of 256256 collectedfrom Brodatz textures, Vistex, Mayang database and also from natural resources from digital
camera. Some of them in Dataset-1 are shown in the Figure. 5. The Dataset-2 consists of
various brick, granite, and marble and mosaic stone textures with resolution of 256256
collected from Outtex, Paulbourke color textures database, and also from natural resources
from digital camera. Some of them in Dataset-2 are shown in the Figure. 6. Dataset-1 and
Dataset-2 contains 80 and 96 original color texture images respectively.
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Figure.5: Input texture
uter Engineering and Technology (IJCET)
Online) Volume 4, Issue 3, May June (2013
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roup of 9 samples of Granite, Brick, Mosaic, a
Dataset-1
, ISSN 0976-
), IAEME
d Marble in
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Figure 6: Input texture group of 12 samples of Mosaic, Granite, Brick, and Marble
with size of 256256 in Dataset-2
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The present paper used Harr wavelet transform due to its advantages as specified in
section 2.1.1.The frequency of occurrence (histogram) of Harr wavelet based texton patterns
of Granite Marble, Mosaic, and Brick texture images in Dataset1 are listed out in Table 1.
The sum of frequency of occurrence of the proposed WHTP method of each input textureimages in Dataset1 are listed out in Table 2.
Table 1: Frequency occurrence of proposed WHTP method for granite. mosaic, marble and
Brick texture in daraset1
S.
No
Granite
Texture Name
Six
textons
frequency mosaic Texture Name
Six
textons
frequency
marble
Texture Name
Six
textons
frequency
Brick
Texture
Name
Six
textons
frequency
1 blue_granite 698 concrete_bricks_170756 116 apollo 1790 Brick.0001 3070
2 blue_pearl 556 concrete_bricks_170757 43 canyon_blue 2230 Brick.0002 3599
3 blue_topaz 611 concrete_bricks_170776 121 cotto 1326 Brick.0003 3547
4 brick_erosion 641 crazy_paving_5091370 72 curry_stratos 1694 Brick.0004 4171
5 canyon_black 719 crazy_paving_5091376 72 flinders_blue 1716 Brick.0005 4046
6 dapple_green 741 crazy_tiles_130356 55 flinders_green 2629 Brick.0006 3351
7 ebony_oxide 586 crazy_tiles_5091369 68 forest_boa 1889 Brick.0007 3256
8 giallo_granite 459 dirty_floor_tiles_ footprints_2564 52 forest_stone 1524 Brick.0008 3565
9 gosford_stone 492 dirty_tiles_200137 125 goldmarble1 2380 Brick.0009 3717
10 greenstone 830 floor_tiles_030849 66 green_granite 2589 Brick.0010 3326
11 interlude_haze 719 grubby_tiles_2565 293 grey_stone 1238 Brick.0011 3487
12 kalahari 889 kitchen_tiles_4270064 264 greymarble1 2564 Brick.0012 3894
13 mesa_twilight 554 moroccan_tiles_030826 118 greymarble3 2511 Brick.0013 3683
14 mesa_verte 690 moroccan_tiles_030857 80 marble001 1055 Brick.0014 4084
15 monza 636 mosaic_tiles_8071010 54 marble018 1373 Brick.0015 3285
16 pietro_nero 605 mosaic_tiles_leaf_pattern_201005060 82 marble034 2078 Brick.0016 4141
17 russet_granite 485 mosaic_tiles_roman_pattern_201005034 266 marble033 2419 Brick.0017 3870
18 granite10 690 motif_tiles_6110065 176 marble012 2512 Brick.0018 3464
19 granite13 779 ornate_tiles_030845 139 marble014 1726 Brick.0019 3381
20 granite20 817 repeating_tiles_130359 296 marble020 1452 Brick.0020 4083
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Table 2: The sum of frequency occurrence of proposed WHTP method for 4 categories of
stone textures in dataset1
Granite mosiac marble brick698 116 1790 3070
556 43 2230 3599
611 121 1326 3547
641 72 1694 4171
719 72 1716 4046
741 55 2629 3351
586 68 1889 3256
459 52 1524 3565
492 125 2380 3717
830 66 2589 3326
719 283 1238 3487
889 264 2564 3894
554 118 2511 3683
690 80 1055 4084
636 54 1373 3285
605 82 2078 4141
485 266 2419 3870
690 176 2512 3464
779 139 1726 3381
817 296 1452 4083
Figure.7: Classification graph of stone textures based on sum of the occurrences of proposed
WHTP method
0500
1000
1500
2000
2500
3000
3500
4000
4500
1 2 3 4 5 6 7 8 9 1011121314151617181920
Granite
mosiac
marble
brick
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The Table 1, 2 and the classification graph of Fig.7, indicates that sum of frequency
occurrences wavelet based textons for granite, marble, mosaic and brick in dataset1 textures
are lying in-between 43 to 296, 459 to 889, 1055 to 2629, and 3070 to 4171 respectively. The
Table 1, Table 2 and the classification graph of Figure.7 indicates a precise and accurateclassification of the considered stone textures.
The frequency of occurrence of proposed WHTP method of granite and mosaic, brick
and marble texture images in dataset1 are listed out in Table 3. The sum of frequency of
occurrence of the proposed WHTP method of each input texture images in dataset2 are listed
out in Table 4.
Table 3: Frequency occurrence of proposed WHTP method for granite, marble, mosaic and
brick textures in daraset2
Sno
Granite
TextureName
Frequencyof WT Marble Texture Name
Frequencyof WT
Mosaic
TextureName
Frequencyof WT Brick Texture Name
Frequencyof WT
1 images_002 2705 blotched_marble_2052007 2159 images_024 627 alternating_brick_3121141 4335
2 images_006 2808 bricklike_marble_2052068 1919 images_027 750 alternating_brick_3121142 4817
3 images_009 2648 coarse_marble_9261512 1593 images_028 953 brick_1241070 3437
4 images_011 2327 dotted_marble_2052053 1416 images_044 865 brick_3141206 6443
5 images_020 2311 dotty_marble_92398723 1434 images_057 815 brick_3141207 3345
6 images_065 2727 faded_marble_9160023 1132 images_065 732 brick_4161585 8243
7 images_024 2303 fine_textured_marble_9181141 1278 images_080 848 brick_and_wood_wall_3141270 4767
8 images_030 2329 fossils_A220534 2220 images_101 811 brick_blotchy_litchen_2562 7463
9 images_032 2803 marble_cracks_circles_4168 1840 images_132 724 brick_closeup_5013216 6281
10 images_033 2690 marble_fossils_4167 2220 images_133 691 brick_detail_6080096 5593
11 images_038 2836 marble_texture_9181134 1934 images_144 210 brick_flooring_1010262 6299
12 images_040 2971 marble_texture_B231063 1541 images_153 201 brick_lichen_closeup_2561 3127
13 images_041 2757 marble_with_fossils_4165 2012 images_158 105 brick_P3012913 4245
14 images_047 2428 marble_with_fossils_4166 1215 images_178 590 brick_removed_plant_2560 6259
15 images_050 2373 marblelike_stone_9261514 1528 images_197 586 brick_square_pattern_9261479 4988
16 images_051 2303 patterned_stone_C050573 1434 images_239 943 brick_texture_221691 6443
17 images_052 2329 rose_coloured_marble_9181131 1132 images_240 433 brick_texture_4161572 3345
18 images_053 2574 rounded_markings_marble_2397234 1567 images_271 984 brick_texture_9181117 8243
19 images_058 2803 rounded_pattern_marble_2052013 1257 images_285 575 brick_wall_3141250 4767
20 images_062 2690 roundy_marble_297234 1130 images_287 691 brick_wall_3141267 3898
21 images_065 2836 shiny_reflective_marblelike_stone_9261513 1278 images_289 210 brick_wall_7070215 7463
22 images_067 2862 specked_marble_9261515 1643 images_290 201 brick_wall_7070225 5593
23 images_068 2950 specked_marble_C050546 2220 images_296 960 brick_wall_7070226 6299
24 images_071 2971 spotty_marble_4142267 1694 images_326 590 brick_wall_7070227 3946
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Table 4: The sum of frequency occurrence of proposed WHTP method for 4 categories of
stone textures in dataset2
Granite mosiac marble brick
2705 627 2159 4335
2808 750 1919 4817
2648 953 1593 3437
2327 865 1416 6443
2311 815 1434 3345
2727 732 1132 8243
2303 848 1278 4767
2329 811 2220 7463
2803 724 1840 6281
2690 691 2220 5593
2836 210 1934 6299
2971 201 1541 3127
2757 105 2012 4245
2428 590 1215 6259
2373 586 1528 4988
2303 943 1434 6443
2329 433 1132 3345
2574 984 1567 8243
2803 575 1257 4767
2690 691 1130 3898
2836 210 1278 7463
2862 201 1643 5593
2950 960 2220 6299
2971 590 1694 3946
Figure.8: Classification graph of stone textures based on sum of the occurrences of proposed
WHTP method
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Granite
mosiac
marble
brick
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828486889092949698
spectral, varianceand wavelet-based
features
Wavelet Transforms
Based on Gaussian
Markov RandomField approach
Proposed WHTP
Method
The Table 3, 4 and the classification graph of Fig.9, indicates that sum of frequency
occurrences proposed WHTP method for granite, marble, mosaic and brick in dataset2
textures are laying in-between 2303 to 2971, 1130 to 2220, 105 to 984, and 3127 to 8243
respectively. The Table 3, Table 4 and the classification graph of Figure.8, indicates a preciseand accurate classification of the considered stone textures.
IV. COMPARISON WITH OTHER METHODSThe proposed WHTP method detections is compared with spectral, variance and
wavelet-based features [29] and GMRF model on linear wavelets [30] methods. The above
methods classified stone textures into three groups only. This indicates that the existing
methods [29, 30] failed in classifying all stone textures. Further the present paper evaluated
mean classification rate using k-nn classifier. The percentage of classification rates of the
proposed WHTP method and crashes methods [29, 30] are listed in table 5. The table 5
clearly indicates that the proposed WHTP method detection outperforms the other existing
methods and did not need any classification technique. Fig.9 shows the comparison chart ofthe proposed wavelet based texton detection with the other existing methods of Table 5.
Table 5: mean % classification rate of the proposed and existing methods
Image Dataset
spectral,
variance and
wavelet-based
features
Wavelet Transforms
Based on Gaussian
Markov Random Field
approach
proposed WHTP
method
Brodatz 88.05 92.19 94.56
VisTex 89.23 92.56 93.15
Outtex 87.76 93.29 96.57
Mayang 90.07 92.86 95.06
Paulbourke 89.66 91.76 95.97
Figure. 9: comparison graph of proposed and existing systems
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V. CONCLUSIONSThe present paper proposed WHTP method to classify the textures among the class of
textures. The present paper used Harr wavelet due to its advantages; However other wavelet
transforms are also yielding the same results. The graphs plotted based on occurrences of textonpatterns clearly classifies and recognizes Brick, Marble, Granite and Mosaic textures precisely. Therecent stone texture Classification methods failed in classifying all the stone textures precisely.
ACKNOWLEDGMENT
I would like to express my cordial thanks to CA. Basha Mohiuddin, Chairman Vidya Groupof Institutions, Chevella, R.R.Dt for providing moral support and encouragement towards research,
Anurag Group of Institutions, Hyderabad and MGNIRSA, Hyderabad for providing necessary
Infrastructure. Authors would like to thank the anonymous reviewers for their valuable comments.And they would like to thank Dr.G.V.S.Ananta Lakshmi, Professor in Dept. of ECS, Anurag Group of
Institutions for her invaluable suggestions and constant encouragement that led to improvise the
presentation quality of this paper
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AUTHORS PROFILE
J. Sasi Kiran Graduated in B.Tech. (EIE) from JNTU University in2002. He received Masters Degree in M.Tech. (C&C), from Bharath
University, Chennai, in 2005 and pursuing Ph.D from University of
Mysore, Mysore in Computer Science under the guidance of Dr V.
Vijaya Kumar. He served as Assistant Professor from 2005 to 2007 and
working as Associate Professor & HOD in CSE Dept., since 2008 at
Vidya Vikas Institute of Technology, Hyderabad. His research interests
include Network Security, Digital Watermarking, and Pattern
Recognition & Image Analysis. He has published research papers in
various National, International conferences, proceedings and Journals. He is a life member of
ISTE, ISC and management committee member of CSI. He has received significant
contribution award from CSI India.
U Ravi Babu obtained his MSc Information Systems (IS) fromAKRG PG College, Andhra University in the year 2003 and M.Tech
Degree from RVD University in the year 2005. He is a member of
SRRF-GIET, Rajahmundry. He is pursuing his Ph.D from AN
University-Guntur in Computer Science & Engineering under the
guidance of Dr V. Vijaya Kumar. He has published research papers in
various National, Inter National conferences, proceedings. He is
working as an Assistant Professor in GIET, Rajahmundry from July
2003 to till date. He is a life member of ISCA
Vakulabharanam Vijaya Kumar received integrated M.S.Engg, degree from Tashkent Polytechnic Institute (USSR) in 1989. He
received his Ph.D. degree in Computer Science from Jawaharlal Nehru
Technological University (JNTU) in 1998. He has served the JNT
University for 13 years as Assistant Professor and Associate Professor
and taught courses for M.Tech students. He has been Dean for Dept of
CSE and IT at Godavari Institute of Engineering and Technology since
April, 2007. His research interests include Image Processing, Pattern
Recognition, Network Security, Steganography, Digital Watermarking, and Image retrieval.
He is a life member for CSI, ISTE, IE, IRS, ACS, ISC, NRSA and CS. He has published
more than 150 research publications in various National, Inter National conferences,proceedings and Journals. He has received best researcher, best teacher award s from JNTUK
Kakinada and Gold plated silver award from Indian Red Cross Society.