Wavelet based histogram method for classification of textu


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Wavelet based histogram method for classification of textu

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME149WAVELET BASED HISTOGRAM METHOD FOR CLASSIFICATIONOF TEXTURESJangala. Sasi Kiran 1, U Ravi Babu 2, Dr. V. Vijaya Kumar 31(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)ABSTRACTTo achieve high accuracy in classification the present paper proposes a new methodon texton pattern detection based on wavelets. Each texture analysis method depends uponhow the selected texture features characterizes image. Whenever a new texture feature isderived it is tested whether it precisely classifies the textures. Here not only the texturefeatures are important but also the way in which they are applied is also important andsignificant for a crucial, precise and accurate texture classification and analysis. That is thereason the present paper applied the derived a new method called Wavelet based Histogramon Texton Patterns (WHTP). So far no exhaustive work was carried out in the waveletdomain for classification of textures, based on histogram of texton pattern extraction. This isthe principal motivation for the work done in this paper. The proposed WHTP method istested on stone textures for precise classification.The proposed texton pattern detectionevaluates the relationship between the values of neighboring pixels in the wavelet domain.The experimental results on various stone textures indicate the efficacy of the proposedmethod when compared to other methods.Key words: Texton, Pattern detection, neighboring pixels, feature extraction, stone textures,multi resolutionINTERNATIONAL 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.comIJCET© I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME150I. INTRODUCTIONTexture analysis plays an important role in many image processing tasks, rangingfrom 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 visiblelight at the pixel’s position, while ignoring the color information. The performance of suchmethods can be improved by adding the color information because, besides texture, color isthe most important property, especially when dealing with real world images [8]. In contrastto intensity, coded as scalar gray values, color is a vectorial feature assigned to each pixel in acolor image. Although the use of color for texture image analysis is shown to beadvantageous, the integration of color and image is still exceptional.The wavelet methods [3, 4, 8, 9] offer computational advantages over other methodsfor texture classification and segmentation. Study of patterns on textures is recognized as animportant step in characterization and classification of texture. Various approaches areexisting 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 themost useful when dealing with regular patterns within image data. It has been used to filterout speckle in radar data [19] and to remove the effects of regular agricultural patterns inimage data [19]. Study of regular patterns based on fundamentals of local variance wascarried out recently [20, 21]. Hence, the study of patterns still plays a significant area ofresearch in classification, recognition and characterization of textures [22].A wavelet transform-based texture classification algorithm has several importantcharacteristics: (1) The wavelet transform is able to decorrelate the data and achieve the samegoal as the linear transformation [23]. (2) The wavelet transform provides orientationsensitive information which is essential in texture analysis. (3) The computational complexityis 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 isbuilt from a training set by k-means clustering algorithm and then an texton distribution iscomputed for a given texture image. The main drawback of this is, it lacks spatialinformation. Texture patterns can provide significant and abundance of texture and shapeinformation. One of the features proposed by Julesz [26, 27] called texton, represents thevarious patterns of image which is useful in texture analysis. In the present paper, Textons aredetected on wavelet decomposed texture image for texture classification. The differenttextons may form various image features.The proposed WHTP method is an extension of our earlier method [28], with multiresolution and robust features. The proposed WHTP method attempted to classify variousHSV-based color stone textures classification based on frequency occurrence of textons inwavelet 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 textonfeature evaluation method. Experimental results and comparison the results with othermethods are discussed in section 3 and conclusions are given in section 4.
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME1512-levelDWTTexton FrequencyExtractionFeatureLibraryII. 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 PatternsIn this paper, the DWT is applied on a set of texture images and texton frequenciesare extracted from the approximation and detail subbands of DWT decomposed images, atdifferent scales. The various combinations of the texton frequencies are applied for textureclassification and a set of best feature vector are chosen. In order to improve the success rateof classification, the texton frequencies are calculated for original image, approximation anddetail sub-bands of 1-level DWT decomposed images. It is found that the success rate isimproved 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 theFrench word ondelette, meaning “small wave.” Soon it was transferred to English bytranslating “onde” into “wave,” giving “wavelet.”Today wavelets play a significant role in Astronomy, Acoustics, Nuclear Engineering,Subband Coding, Signal and Image Processing, Neurophysiology, Music, MagneticResonance Imaging, Speech Discrimination, Optics, Turbulence, Earthquake Prediction,Radar, Computer and Human Vision, Data Mining and Pure Mathematics Applications suchas Solving Partial Differential Equations etc.The most commonly used transforms are the Discrete Cosine Transform (DCT), DiscreteFourier Transform (DFT), Discrete Wavelet Transform (DWT), Discrete Laguerre Transform(DLT) and the Discrete Hadamard Transform (DHT). The DCT is favoured in the earlyimage and video processing. There are large numbers of image processing algorithms that useDCT routines. DCT based image processing techniques are robust compared to spatialdomain techniques. The DCT algorithms are robust against simple image processingoperations 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 themost popular and widely used compression methods. The quality of the reconstructed imagesin DCT is degraded by the “false contouring” effect for specific images having graduallyOriginal TextureImages
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME152shaded areas. The false contouring occurs in DCT when smoothly graded area of an image isdistorted by an aberration due to heavy quantization of the transform coefficients. The effectlooks like a contour map. Due to this reason, the DCT based image processingmethods are weak against geometric attacks like rotation, scaling, cropping etc.To overcome the above drawbacks, the present paper adopted DWT techniques toachieve better performance. The Discrete wavelet transform (DWT) is a powerful tool ofsignal and image processing that have been successfully used in many scientific fields such assignal processing, image compression, image segmentation, computer graphics, and patternrecognition .The DWT based algorithms, has been emerged as another efficient tool for imageprocessing, mainly due to its ability to display image at different resolutions and to achievehigher compression ratio. In DWT, signal energy concentrates to specific waveletcoefficients. This characteristic feature is useful for multi-resolution analysis. DWT providessufficient information both for analysis and synthesis of the original signal, with a significantreduction 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 compactlysupported orthogonal wavelets. These wavelets along with Meyer wavelets are capable ofperfect 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 aparticular application.2.1.1 Salient features of Haar wavelet transformThe Haar wavelet is the first known wavelet. The Haar wavelet transform has anumber 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 wavelettransforms.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, HL1and HH1 represent the finest scale wavelet coefficients i.e., detail images while the sub-bandLL1corresponds to coarse level coefficients i.e., approximation image. To obtain the nextcoarse level of wavelet coefficients, the sub-band LL1 alone is further decomposed andcritically 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 untilsome 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 anddiscrimination. In this paper/thesis Haar wavelet, Daubechies wavelets, and Symlet waveletare used for decomposition.
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME153(2a) (2b)Figure 2: DWT Decomposition:2(a) First level of DWT 2(b) second level of DWT2.2 Texton detectionTextons [26, 27] are considered as texture primitives, which are located with certainplacement rules. A close relationship can be obtained with image features such as shape,pattern, local distribution orientation, spatial distribution, etc.., using textons. The textons aredefined 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 andaccurate texture classification, the present study strongly believes that one need to considerall different textons. That is the reason the present study considered all. There are severalissues related with i) texton size ii) tonal difference between the size of neighbouring pixelsiii) texton categories iv) expansion of textons in one orientation v) elongated elements oftextons with jittered in orientation . By this some times a fine or coarse or an obvious shapemay results or a pre-attentive discrimination is reduced or texton gradients at the textureboundaries may be increased. To address this, the present paper utilized six texton types on a2×2 grid as shown in Figure 3(a). In Figure 3(a), the four pixels of a 2×2 grid are denoted asV1, V2, V3 and V4. If two pixels are highlighted in gray color of same value in subband imagethen the grid will form a texton. The six texton types denoted as TP1, TP2, TP3, TP4, TP5 andTP6 are shown in Figure 3(b) to 3(g).V1 V2V3 V4(a) (b) (c) (d)(e) (f) (g)Figure 3: Six special types of Textons:a) 2×2 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 Figure4. The present paper conducted experiments using Harr wavelet transform due to itsadvantage as specified in the section 2.1.1. First, the original image is decomposed usingLL1 HL1LH1 HH1LL2 HL2HL1LH2 HH2LH1 HH1
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME154Haar, transform. On the approximation subband image, textons are identified. Thenthe frequency occurrences of all six different textons as shown in Figure 4, with differentorientations are evaluated. To have a precise and accurate texture classification, the presentstudy considered sum of the frequencies of occurrences of all six different textons as shownin Figure 3 on a 2×2 block.TP2 TP1 0 3 0 0 2 23 1 0 0 0 3TP3 0 0 3 4 0 00 0 3 2 0 0TP4 TP4 TP3 5 0 4 3 1 01 5 2 4 1 5(d) (e)Figure 4: Illustration of the texton pattern detection process:(a) 2×2 grid (b) wavelet transformed image (c) & (d) Texton location and texton types (e)Texton imageIII. RESULTS AND DISCUSSIONSExperiments are carried out on the proposed WHTP method to demonstrate theeffectiveness of the proposed method for stone texture classification. The proposed methodWHTP paper carried out the experiments on two Datasets. The Dataset-1 consists of variousbrick, granite, and marble and mosaic stone textures with resolution of 256×256 collectedfrom Brodatz textures, Vistex, Mayang database and also from natural resources from digitalcamera. Some of them in Dataset-1 are shown in the Figure. 5. The Dataset-2 consists ofvarious brick, granite, and marble and mosaic stone textures with resolution of 256×256collected from Outtex, Paulbourke color textures database, and also from natural resourcesfrom digital camera. Some of them in Dataset-2 are shown in the Figure. 6. Dataset-1 andDataset-2 contains 80 and 96 original color texture images respectively.
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, MayFigure.5: Input texture group of 9 samples of Granite, Brick, Mosaic, and Marble inJournal of Computer Engineering and Technology (IJCET), ISSN 09766375(Online) Volume 4, Issue 3, May – June (2013), © IAEME155Input texture group of 9 samples of Granite, Brick, Mosaic, and Marble inDataset-1Journal of Computer Engineering and Technology (IJCET), ISSN 0976-June (2013), © IAEMEInput texture group of 9 samples of Granite, Brick, Mosaic, and Marble in
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME156Figure 6: Input texture group of 12 samples of Mosaic, Granite, Brick, and Marblewith size of 256×256 in Dataset-2
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME157The present paper used Harr wavelet transform due to its advantages as specified insection 2.1.1.The frequency of occurrence (histogram) of Harr wavelet based texton patternsof 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 andBrick texture in daraset1S.NoGraniteTexture NameSixtextonsfrequency mosaic Texture NameSixtextonsfrequencymarbleTexture NameSixtextonsfrequencyBrickTextureNameSixtextonsfrequency1 blue_granite 698 concrete_bricks_170756 116 apollo 1790 Brick.0001 30702 blue_pearl 556 concrete_bricks_170757 43 canyon_blue 2230 Brick.0002 35993 blue_topaz 611 concrete_bricks_170776 121 cotto 1326 Brick.0003 35474 brick_erosion 641 crazy_paving_5091370 72 curry_stratos 1694 Brick.0004 41715 canyon_black 719 crazy_paving_5091376 72 flinders_blue 1716 Brick.0005 40466 dapple_green 741 crazy_tiles_130356 55 flinders_green 2629 Brick.0006 33517 ebony_oxide 586 crazy_tiles_5091369 68 forest_boa 1889 Brick.0007 32568 giallo_granite 459 dirty_floor_tiles_footprints_2564 52 forest_stone 1524 Brick.0008 35659 gosford_stone 492 dirty_tiles_200137 125 goldmarble1 2380 Brick.0009 371710 greenstone 830 floor_tiles_030849 66 green_granite 2589 Brick.0010 332611 interlude_haze 719 grubby_tiles_2565 293 grey_stone 1238 Brick.0011 348712 kalahari 889 kitchen_tiles_4270064 264 greymarble1 2564 Brick.0012 389413 mesa_twilight 554 moroccan_tiles_030826 118 greymarble3 2511 Brick.0013 368314 mesa_verte 690 moroccan_tiles_030857 80 marble001 1055 Brick.0014 408415 monza 636 mosaic_tiles_8071010 54 marble018 1373 Brick.0015 328516 pietro_nero 605 mosaic_tiles_leaf_pattern_201005060 82 marble034 2078 Brick.0016 414117 russet_granite 485 mosaic_tiles_roman_pattern_201005034 266 marble033 2419 Brick.0017 387018 granite10 690 motif_tiles_6110065 176 marble012 2512 Brick.0018 346419 granite13 779 ornate_tiles_030845 139 marble014 1726 Brick.0019 338120 granite20 817 repeating_tiles_130359 296 marble020 1452 Brick.0020 4083
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME158Table 2: The sum of frequency occurrence of proposed WHTP method for 4 categories ofstone textures in dataset1Granite mosiac marble brick698 116 1790 3070556 43 2230 3599611 121 1326 3547641 72 1694 4171719 72 1716 4046741 55 2629 3351586 68 1889 3256459 52 1524 3565492 125 2380 3717830 66 2589 3326719 283 1238 3487889 264 2564 3894554 118 2511 3683690 80 1055 4084636 54 1373 3285605 82 2078 4141485 266 2419 3870690 176 2512 3464779 139 1726 3381817 296 1452 4083Figure.7: Classification graph of stone textures based on sum of the occurrences of proposedWHTP method0500100015002000250030003500400045001 2 3 4 5 6 7 8 9 1011121314151617181920Granitemosiacmarblebrick
  11. 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME159The Table 1, 2 and the classification graph of Fig.7, indicates that sum of frequencyoccurrences wavelet based textons for granite, marble, mosaic and brick in dataset1 texturesare lying in-between 43 to 296, 459 to 889, 1055 to 2629, and 3070 to 4171 respectively. TheTable 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, brickand marble texture images in dataset1 are listed out in Table 3. The sum of frequency ofoccurrence of the proposed WHTP method of each input texture images in dataset2 are listedout in Table 4.Table 3: Frequency occurrence of proposed WHTP method for granite, marble, mosaic andbrick textures in daraset2SnoGraniteTextureNameFrequencyof WT Marble Texture NameFrequencyof WTMosaicTextureNameFrequencyof WT Brick Texture NameFrequencyof WT1 images_002 2705 blotched_marble_2052007 2159 images_024 627 alternating_brick_3121141 43352 images_006 2808 bricklike_marble_2052068 1919 images_027 750 alternating_brick_3121142 48173 images_009 2648 coarse_marble_9261512 1593 images_028 953 brick_1241070 34374 images_011 2327 dotted_marble_2052053 1416 images_044 865 brick_3141206 64435 images_020 2311 dotty_marble_92398723 1434 images_057 815 brick_3141207 33456 images_065 2727 faded_marble_9160023 1132 images_065 732 brick_4161585 82437 images_024 2303 fine_textured_marble_9181141 1278 images_080 848 brick_and_wood_wall_3141270 47678 images_030 2329 fossils_A220534 2220 images_101 811 brick_blotchy_litchen_2562 74639 images_032 2803 marble_cracks_circles_4168 1840 images_132 724 brick_closeup_5013216 628110 images_033 2690 marble_fossils_4167 2220 images_133 691 brick_detail_6080096 559311 images_038 2836 marble_texture_9181134 1934 images_144 210 brick_flooring_1010262 629912 images_040 2971 marble_texture_B231063 1541 images_153 201 brick_lichen_closeup_2561 312713 images_041 2757 marble_with_fossils_4165 2012 images_158 105 brick_P3012913 424514 images_047 2428 marble_with_fossils_4166 1215 images_178 590 brick_removed_plant_2560 625915 images_050 2373 marblelike_stone_9261514 1528 images_197 586 brick_square_pattern_9261479 498816 images_051 2303 patterned_stone_C050573 1434 images_239 943 brick_texture_221691 644317 images_052 2329 rose_coloured_marble_9181131 1132 images_240 433 brick_texture_4161572 334518 images_053 2574 rounded_markings_marble_2397234 1567 images_271 984 brick_texture_9181117 824319 images_058 2803 rounded_pattern_marble_2052013 1257 images_285 575 brick_wall_3141250 476720 images_062 2690 roundy_marble_297234 1130 images_287 691 brick_wall_3141267 389821 images_065 2836 shiny_reflective_marblelike_stone_9261513 1278 images_289 210 brick_wall_7070215 746322 images_067 2862 specked_marble_9261515 1643 images_290 201 brick_wall_7070225 559323 images_068 2950 specked_marble_C050546 2220 images_296 960 brick_wall_7070226 629924 images_071 2971 spotty_marble_4142267 1694 images_326 590 brick_wall_7070227 3946
  12. 12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME160Table 4: The sum of frequency occurrence of proposed WHTP method for 4 categories ofstone textures in dataset2Granite mosiac marble brick2705 627 2159 43352808 750 1919 48172648 953 1593 34372327 865 1416 64432311 815 1434 33452727 732 1132 82432303 848 1278 47672329 811 2220 74632803 724 1840 62812690 691 2220 55932836 210 1934 62992971 201 1541 31272757 105 2012 42452428 590 1215 62592373 586 1528 49882303 943 1434 64432329 433 1132 33452574 984 1567 82432803 575 1257 47672690 691 1130 38982836 210 1278 74632862 201 1643 55932950 960 2220 62992971 590 1694 3946Figure.8: Classification graph of stone textures based on sum of the occurrences of proposedWHTP method01000200030004000500060007000800090001 2 3 4 5 6 7 8 9 101112131415161718192021222324Granitemosiacmarblebrick
  13. 13. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME161828486889092949698spectral, varianceand wavelet-basedfeaturesWavelet TransformsBased on GaussianMarkov RandomField approachProposed WHTPMethodThe Table 3, 4 and the classification graph of Fig.9, indicates that sum of frequencyoccurrences proposed WHTP method for granite, marble, mosaic and brick in dataset2textures are laying in-between 2303 to 2971, 1130 to 2220, 105 to 984, and 3127 to 8243respectively. 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 andwavelet-based features [29] and GMRF model on linear wavelets [30] methods. The abovemethods classified stone textures into three groups only. This indicates that the existingmethods [29, 30] failed in classifying all stone textures. Further the present paper evaluatedmean classification rate using k-nn classifier. The percentage of classification rates of theproposed WHTP method and crashes methods [29, 30] are listed in table 5. The table 5clearly indicates that the proposed WHTP method detection outperforms the other existingmethods 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 methodsImage Datasetspectral,variance andwavelet-basedfeaturesWavelet TransformsBased on GaussianMarkov Random Fieldapproachproposed WHTPmethodBrodatz 88.05 92.19 94.56VisTex 89.23 92.56 93.15Outtex 87.76 93.29 96.57Mayang 90.07 92.86 95.06Paulbourke 89.66 91.76 95.97Figure. 9: comparison graph of proposed and existing systems
  14. 14. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME162V. CONCLUSIONSThe present paper proposed WHTP method to classify the textures among the class oftextures. The present paper used Harr wavelet due to its advantages; However other wavelettransforms 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.ACKNOWLEDGMENTI 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 necessaryInfrastructure. 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 ofInstitutions for her invaluable suggestions and constant encouragement that led to improvise thepresentation quality of this paperREFERENCES[1] A. Bovik , M. Clark , W. S. Geisler, “Multichannel Texture Analysis Using Localized SpatialFilters”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1), pp. 55-73,(1990 ).[2] A. K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters”,Pattern Recognition, 24(12), pp. 1167-1186( 1991).[3] A. Laine and J. Fan, “Texture classification by wavelet packet signatures”, IEEE Trans. onPAMI, 15(11), pp. 1186—1190(1993).[4] Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I. (1992). Image coding using wavelettransform. IEEE Trans. Image Processing, Vol.1 (2), pp. 205–220.[5] Brodatz, P., “Textures: A Photographic Album for Artists and Designers”, New York:Dover,New York, 1966.[6] Daubechies, I. (1992). Ten Lectures on Wavelets. Rutgers University and AT&TLaboratories.[7] Jin Xie, Lei Zhang, Jane You And David Zhang , “Texture Classification Via Patch-BasedSparse Texton Learning”[8] G. Van de Wouwer, P. Scheunders, S. Livens, and D. Van Dyck, “Wavelet CorrelationSignatures for Color Texture Characterization”, Pattern Recognition, 32(3)(1999), pp. 443–451.[9] E. Montiel, A. S. Aguado, M. S. Nixon, “Texture classification via conditional histograms”,Pattern Recognition Letters, 26, pp. 1740-1751(2005).[10] Richards, J. A. and Xiuping, J. (1999). Remote Sensing Digital Analysis: An Introduction.Germany: Springer- Verlag, vol.3, pp.363-363.[11] Moody, A. and Johnson, D. M. (2001). Land-surface phenologies from AVHRR using thediscrete fourier transform. Remote Sens. Environ., vol. 75, pp. 305-323.[12] Zhang, M., Carder, K. and Muller, karger. (1999). Noise reduction and atmosphericcorrection for coastal applications of landsat thematic-mapper imagery. Remote Sens.Environ., vol. 70, pp. 167-180[13] Burrough, P. A. (1983). Multiscale sources of spatial variation in soil, the application offractal concepts to nested levels of soil variation. Journal of Soil Sci., vol. 34, pp. 577-597.[14] Atkinson, P. M. and Lewis, P. (2000). Geostatistical classification for remote sensing: Anintroduction. Comput. Geo. sci., Vol. 26, pp. 361-371.
  15. 15. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME163[15] Curran, P. J. (1988). The Semivariogram in Remote Sensing: An Introduction. Remote Sens.Environ., vol. 24, pp. 493-507.[16] Treitz, P. (2001). Variogram analysis of high spatial resolution remote sensing data: Anexamination of boreal forest ecosystems. Int. J. Remote Sens., vol. 22, pp. 3895-3900.[17] Woodcock, C. E., Strahler, A. H. and Jupp, D. L. (1988). The use of variograms in remotesensing II: Real digital images. Remote Sens. Environ., vol. 25, pp. 349-379.[18] Moody, A. and Johnson, D. M. (2001). Land-surface phenologies from AVHRR using thediscrete fourier transform. Remote Sens. Environ., vol. 75, pp. 305-323.[19] McCloy, K.R. (2002). Analysis and removal of the effects of crop management practices inremotely sensed images of agricultural fields. Int. J. Remote Sens., vol. 23, pp. 403-416.[20] Peder, Klith Bocher. and Keith, R. McCloy. (2006). The Fundamentals of Average LocalVariance: Detecting Regular Patterns. IEEE Trans. on Image Processing, vol. 15, pp. 300-310.[21] Suresh A. and Vijaya Kumar V. et al. (2007). Texture Classification by Simple Patterns onEdge Direction Movements. International Journal of Computer Science and NetworkSecurity, vol. 7, no. 11, pp. 221-225.[22] Suresh A. and Vijaya Kumar V. et al. (2008). Classification of Textures by AvoidingComplex Patterns. Journal of Computer Science, Science Publications, USA, vol. 4(2),pp.133-138.[23] T. Chang, C. C. Jay Kuo, “Texture analysis and classification with tree-structured wavelettransform”, IEEE Trans. Image Process, 2(4), pp. 429-441(1993).[24] Zhenhua Guo, Qin Li, Lin Zhang, Jane You, Wenhuang Liu, and Jinghua Wang, “TextureImage Classification Using Complex Texton”, ICIC 2011, LNAI 6839, pp. 98–104, 2012.Springer-Verlag Berlin Heidelberg 2012[25] B.V. Ramana Reddy, M.Radhika Mani, B.Sujatha, and Dr.V.Vijaya Kumar “TextureClassification Based on Random Threshold Vector Technique”, International Journal ofMultimedia and Ubiquitous Engineering Vol. 5, No. 1, January, 2010.[26] Julesz B., ―Textons, The Elements of Texture Perception, and their Interac-tions,” Nature,vol.290 (5802): pp.91-97, 1981.[27] Julesz B., ―Texton gradients: the texton theory revisited,” Biological Cybernet-ics, vol.54pp.245–251, 1986.[28] U Ravi Babu, Dr V Vijaya Kumar, B Sujatha, “Texture Classification Based on TextonFeatures” International Journal of Image, Graphics & Signal Processing on vol. 4, number:8, 2012. Pages:36-42.[29] J. Chen, D. Chen, and D. Blostein “Wavelet-Based Classification of Remotely SensedImages: A Comparative Study of Different Feature Sets in an Urban Environment”, Journal ofEnvironmental Informatics 10(1) 2-9 (2007).[30] B.V. Ramana Reddy, M. Radhika Mani, and K.V. Subbaiah, “Texture Classification Methodusing Wavelet Transforms Based on Gaussian Markov Random Field” International Journalof Signal and Image Processing Vol.1-2010/Iss.1 pp. 35-39.[31] R. Edbert Rajan and Dr.K.Prasadh, “Spatial and Hierarchical Feature Extraction Based on Siftfor Medical Images”, International Journal of Computer Engineering & Technology (IJCET),Volume 3, Issue 2, 2012, pp. 308 - 322, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.[33] Gopal Thapa, Kalpana Sharma and M.K.Ghose, “Multi Resolution Motion EstimationTechniques For Video Compression: A Survey”, International Journal of ComputerEngineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 399 - 406, ISSN Print:0976 – 6367, ISSN Online: 0976 – 6375.[34] Abhishek Choubey , Omprakash Firke and Bahgwan Swaroop Sharma, “Rotation andIllumination Invariant Image Retrieval using Texture Features”, International Journal ofElectronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2,2012, pp. 48 - 55, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.
  16. 16. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME164AUTHORS PROFILEJ. Sasi Kiran Graduated in B.Tech. (EIE) from JNTU University in2002. He received Masters Degree in M.Tech. (C&C), from BharathUniversity, Chennai, in 2005 and pursuing Ph.D from University ofMysore, Mysore in Computer Science under the guidance of Dr V.Vijaya Kumar. He served as Assistant Professor from 2005 to 2007 andworking as Associate Professor & HOD in CSE Dept., since 2008 atVidya Vikas Institute of Technology, Hyderabad. His research interestsinclude Network Security, Digital Watermarking, and PatternRecognition & Image Analysis. He has published research papers invarious National, International conferences, proceedings and Journals. He is a life member ofISTE, ISC and management committee member of CSI. He has received significantcontribution award from CSI India.U Ravi Babu obtained his MSc Information Systems (IS) fromAKRG PG College, Andhra University in the year 2003 and M.TechDegree from RVD University in the year 2005. He is a member ofSRRF-GIET, Rajahmundry. He is pursuing his Ph.D from ANUniversity-Guntur in Computer Science & Engineering under theguidance of Dr V. Vijaya Kumar. He has published research papers invarious National, Inter National conferences, proceedings. He isworking as an Assistant Professor in GIET, Rajahmundry from July2003 to till date. He is a life member of ISCAVakulabharanam Vijaya Kumar received integrated M.S.Engg, degree from Tashkent Polytechnic Institute (USSR) in 1989. Hereceived his Ph.D. degree in Computer Science from Jawaharlal NehruTechnological University (JNTU) in 1998. He has served the JNTUniversity for 13 years as Assistant Professor and Associate Professorand taught courses for M.Tech students. He has been Dean for Dept ofCSE and IT at Godavari Institute of Engineering and Technology sinceApril, 2007. His research interests include Image Processing, PatternRecognition, 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 publishedmore than 150 research publications in various National, Inter National conferences,proceedings and Journals. He has received best researcher, best teacher award s from JNTUKKakinada and Gold plated silver award from Indian Red Cross Society.