A Study on Discrete Wavelet Transform based Texture Feature Extraction for Image Mining

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1 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), A Study on Dscrete Wavelet Transform based Texture Feature Extracton for Image Mnng Dr. T. Karthkeyan 1, P. Mankandaprabhu 1 Assocate Professor, Research Scholar, Department of Computer Scence, PSG College of Arts & Scence, Combatore. manpsgphd@gmal.com Abstract Ths paper proposed the dscrete wavelet based texture features n Image Mnng. The Proposed methodology uses the Dscrete Wavelet Transform to reduce the sze of test mages. Grey Level Co-occurrence Matrx (GLCM) s appled for all test mages of Low Level components of level decomposed mages to extract the texture feature of the mages. Related mages are retreved by usng dfferent dstance measure classfers. The expermental result shows that the proposed method acheves comparable retreval performance for correlaton property of GLCM of texture feature. Keywords - Texture, Dscrete Wavelet Transform, gray level co-occurrence matrx, Dstance Measures. 1. Introducton The open spread use of dgtal and multmeda knowledge, storeroom; fndng and recovery of mages begnnng the huge database become not easy. To facltate economcal searchng and retrevng of pctures as of the dgtal collecton, new software and technques have been emerged. The need to dscover a preferred mage from a huge collecton s mutual by many sklled groups ncludng the meda persons, drawng engneers, art hstorans and scholars etc. Content Based Image Retreval (CBIR) s compared wth text or content related advance for recover smlar mages from the database [4, 5]. Content Based Image Retreval (CBIR) does not need manual annotaton for each mage and s not ncomplete by the avalablty of lexcons as a substtute ths framework utlzes the low level features that are natural n the mages, color, shape and texture. In CBIR, some forms of parallel between mages are computed usng mage futures extracted from them. Thus, users can look for mages ust lke query mages quckly and effectvely. Fg. 1 shows the archtecture of a typcal CBIR system. For each mage n the mage database and ts mage features are extracted and the obtaned feature space (or vector) s stored n the feature database. once a query mage comes n, ts feature space are gong to be compared wth those wthn the feature database one by one and the smlar mages wth the smallest feature dstance wll be retreved. Image DB Feature Database Feature Extracton Query Image Query Features Smlarty Measures Retreved Images Fg.1: Image Retreval Process CBIR may be dvded n the followng stages: Preprocessng: The mage s frst processed n order to extract the features to descrbe the contents. The processng nvolves normalzaton, flterng, segmentaton and obect dentfcaton. The output of ths stage could be a set of sgnfcant regons and obects. Feature extracton: Features such as color, shape, texture, etc. are used to descrbe the content of the mage. Image features can be classfed nto prmtves.. Feature Extracton For the gven mage database [1], features are extracted frst from ndvdual mages. The vsual features lke color, shape, texture or spatal features or Avalable onlne@ 1805

2 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), some compressed doman features. The extracted features are delneatng the feature vectors. These feature vectors are then stored to form mage feature database. For a gven query mage, we smlarly extract ts features and form a feature vector. Ths feature vector s matched wth the already stored vectors n mage feature database. Sometmes dmensonalty reducton technques are employed to reduce the computatons. The dstance between the feature vector of the query mage and those of the mages n the database s then calculated. The dstance of a query mage wth tself s zero f t s n database. Then, the dstances are stored n ncreasng order and retreval s performed wth the help of ndexng scheme. The feature s dstnct as a role of one or more capacty, every of whch specfes some expermental property of an obect and t quantfes some sgnfcant characterstcs of the obect. We classfy the varous features currently employed as follows: General features: Functon self-regulatng features such as shape, color and texture. Independent of the abstracton level, they can be advance n dvded nto: - Pxel level: Features consdered at each pxel level, e.g. locaton, colour. - Local features: Features consdered above the outcome of results s subdvson of the mage band on mage segmentaton or edge detecton. - Global level features: Features measured over the whole mage or smply expected sub-area of an mage. Doman-specfc level: Applcaton relant features lke human faces, fngerprnts, and conceptual features. These features are typcally a synthess of low-level features for a some specfc doman. On the other hand, all features can be closely secret nto low level features and hgh level features. Low level features can be extracted drectly from the orgnal mages, whereas hgh-level feature extracton must be based on low level features []. The vtal problems of content based mage retreval system, whch are:. Image database selecton,. Smlarty measurement,. Performance evaluaton of the retreval process and v. Low-level mage features extracton. 3. Wavelet Transform Wavelet transform has a good locaton property n tme and frequency doman and s exactly wthn the drecton of transform compresson dea. The dscrete wavelet transforms states to wavelet transforms that the wavelets are dsontedly apprased. A transform whch lmts a functon both n space and scalng and has some necessary propertes compared to the Fourer transform. The transform s centred on a wavelet matrx, whch can be fgured more quckly than the analogous Fourer matrx. Most notably, the DWT s used for sgnal codng, where the assets of the transform are exploted to sgnfy a dscrete sgnal n an extra redundant form, often as a precondtonng for data compresson. The dscrete wavelet transform has a vast quantty of applcatons n Scence, Computer Scence, Mathematcs and Engneerng. Wavelets are functons that satsfy certan mathematcal requrements and are used n representng data or other functons. The basc awareness of the wavelet transform s to exemplfy any arbtrary sgnal X as a superposton of a regular of such wavelets or bass functons. These bass functons are ganed from a sngle photo type wavelet called the mother wavelet by dlaton (scalng) and translaton (shfts). The dscrete wavelet transform for two dmensonal sgnals can be defned as follows. 1 X b1 Y b w( a1, a,b 1,b ), a a1 a (1) Where, a= a1a The ndexes equaton.(1) w (a1, a, b1, b) are called wavelet coeffcents of sgnal X and a1, a are dlaton & b1, b are translaton, ψ s the transformng functon s known as mother wavelet. Low frequences are examned wth low temporal resoluton whle hgh frequences wth more temporal resoluton. A wavelet transform combnes both low pass and hgh pass flterng n spectral decomposton of sgnals. In case of dscrete wavelet, the mage s decomposed nto a dscrete set of wavelet coeffcents usng an orthogonal set of basc functons. These sets are dvded nto four parts such as approxmaton, horzontal detals, vertcal detals and dagonal detals. Dscrete Wavelet transform [3] provde substantal mprovement n pcture qualty at hgher compresson rato. The Embedded Zero tree Wavelet codng s a smple, effectve progressve mage codng algorthm and can be worn for both lossless and lossy compresson systems. Ths algorthm works well wth the proposed codng scheme because the zero tree structure s effectve n descrbng the sgnfcance map of the transform coeffcents, as t explots the nherent selfsmlarty of the subband mage over the range of scales, and the postonng of maorty of zero valued coeffcents n the hgher frequency subbands. The EZW algorthm apples Successve Approxmaton Quantzaton n order to provde mult-precson representaton of the transformed coeffcents and to facltate the embedded codng. The algorthm codes Avalable onlne@ 1806

3 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), the transformed coeffcents n decreasng order n several scans. Each scan of the algorthm conssts of two passes: sgnfcant map encodng and refnement pass. The domnant pass scans the subband structure n zgzag, rght-to-left and then top-to-bottom wthn each scale, before proceedng to the next hgher scale of subband structure as presented n Fg.. For each and every pass, a threshold (T) s chosen aganst whch all the coeffcents are measured and encoded as one of the followng four symbols, Sgnfcant postve If the coeffcent value s greater than threshold T Sgnfcant negatve If the magntude of the coeffcent value s greater than threshold T Zero tree root A coeffcent s encoded as zero tree root f the coeffcent and all ts descendants are nsgnfcant wth respect to threshold T Isolated zero If the coeffcent s nsgnfcant but some of ts descendants are sgnfcant. log C T max 0 () where equaton.() Cmax s the maxmum coeffcent n the subband structure. The successve approxmaton quantzaton uses a monotoncally decreasng set of thresholds and encodes the transformed coeffcents as one of the above four labels wth respect to any gven threshold. For successve sgnfcant pass encodng, the T threshold s lowered as T K 1 K and only those coeffcents not yet found to be sgnfcant n the prevous pass are scanned for encodng, and the process s repeated untl the threshold reaches zero, and results n complete encoded bt streams. Fg.: EZW subband structure scannng order In the embedded zero tree wavelet codng strategy, developed by Shapro, a wavelet/subband decomposton of the mage s performed. The wavelet coeffcents/pxels are then grouped nto Spatal Orentaton Trees. The magntude of each wavelet coeffcents/pxels n a tree, startng wth the root of the tree, s then compared to a partcular threshold T. If the magntude of all the wavelet coeffcents/pxels n the tree are smaller than T, the entre tree structure (that s the root and all ts descendant nodes) s coded by one symbol, the zerotree symbol ZTR. If however, there ext sgnfcant wavelet coeffcents/pxels, then the tree root s coded as beng sgnfcant or nsgnfcant, f ts magntude s larger than or smaller than T, respectvely. The descendant nodes are then each examned n turn to determne whether each s the root of a possble sub zero tree structure, or not. Ths process s carred out such that all the nodes n all the trees are examned for possble sub zero tree structures. The sgnfcant wavelet coeffcents/pxels n a tree are coded by one of two symbols, POS or NEG, dependng on whether ther actual values are postve or negatve, respectvely. The process of classfyng the pxels as beng ZTR, IZ, POS, or NEG s referred to as the domnant pass n [4]. Ths s then followed by the subordnate pass n whch the sgnfcant wavelet coeffcents/pxels n the mage are refned by determnng whether ther magntudes le wthn the ntervals (T, 3T/) and (3T/,T). Those wavelet coeffcents/pxels whose magntudes le n the nterval (T, 3T/) are represented by a 0 (LOW), whereas those wth magntudes lyng n the nterval (3T/,T) are represented by a 1 (HIGH). Subsequent to the completon of both the domnant and subordnate passes, the threshold value T s reduced by a factor of, and the entre process repeated. Ths codng strategy, consstng of the domnant and subordnate passes followed by the reducton n the threshold value, s terated untl a target bt rate s acheved. The root node of each tree s located at the hghest level of the decomposton pyramd, and all ts descendants are located n dfferent spatal frequency bands at the same pyramd level, or clustered n groups of X at lower levels of the decomposton pyramd. An EZW decoder reconstructs the mage by progressvely updatng the values of each wavelet coeffcent/pxel n a tree as t receves the data. The decoder's decsons are always synchronzed to those of the encoder. 4. Texture Features Among totally dfferent vsual characterstcs lke color and shape for the analyss of varous types of mages, texture s reported to be outstandng and very mportant low level feature [5, 6]. Even though no standard defnton exsts for texture, Sklansky [7] outlned the texture collecton of natve propertes among the mage regon wth a contnung, slowly vared or about perodc pattern. Texture gves the nformaton on structural arrangement of surfaces and Avalable onlne@ 1807

4 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), obects on the mage. Texture s not defned for a separate pxel; t depends on the dstrbuton of ntensty over the mage. Texture possesses regularty and scalablty propertes; t s represented by man drectons, contrast and sharpness. It s measured usng ts dstnct propertes lke perodcty, coarseness, drectonalty and pattern complexty for effcent mage retreval partcularly on the aspects of orentaton and scale [8]. Tuceryan and Jan [9] dvded the dfferent methods for feature extracton nto four man categores, namely: structural, statstcal, model-based and transform doman. Bascally, texture representaton methods can be classfed nto two categores: structural and statstcal. Statstcal methods, ncludng Fourer power spectra, co-occurrence matrces, shft-nvarant prncpal component analyss (SPCA), Tamura features, Wold decomposton[10], Markov random feld[11], fractal model[1] and mult-resoluton flterng technques such as Gabor[13] and wavelet transform[14], characterze texture by the statstcal dstrbuton of the mage ntensty. D. A. Claus et. al [15] desgned the fuson texture feature wth Gabor flter and co occurrence probabltes for texture segmentaton and demonstrated that t outperforms well for nosy mages and the hgh dmensonal feature vector. The DWT based color cooccurrence feature for texture classfcaton s explaned n [16]. Haralck et. al [17] proposed the methods for representng texture features of mages was grey level co-occurrence matrces (GLCM). Haralck et. al [17] also suggested 14 descrptors ncludng the contrast, correlaton, entropy and others. Each descrptor shows one texture property. Therefore, many works for example as descrbed n [18], are devoted to selectng those statstcal descrptors derved from the cooccurrence matrces that descrbe texture wthn the best approach. In [19], frstly, transformng color space from RGB model to HSI model and then extractng color hstogram to form color feature vector. Secondly, extractng the texture feature by usng gray cooccurrence matrx. The texture of mage s an llustraton of spatal relatonshp of gray level mage. Co-occurrence matrx s make t up based on the pont of reference and dstance between mage pxels. The co-occurrence matrx C(, ) counts the co-occurrence of pxels wth gray values and at a gven dstance d. The dstance d s outlned n polar coordnates (d, ), wth dscrete length and orentaton. In practce, takes the values 0 ; 45 ; 90 ; 135 ; 180 ; 5 ; 70 ; and 315. The cooccurrence matrx C(, ) can now be defned as follows: (( x1, y1),( x, y)) ( XY ) ( XY ) for f ( x1, y1 ), f ( x, y) C(, ) card (3) ( x, y) ( x1, y1) ( d cos, d sn ); for 0<, < where card {.} denotes the number of elements n the set. Let G be the number of gray-values n the mage, then the dmenson of the co-occurrence matrx C (, ) wll be N N. So, the computatonal complexty of the co-occurrence matrx depends quadratcally on the number of gray-scales used for quantzaton. A. Wavelet-Based Texture Representaton In wavelet based texture Representatons, a specfc feature of ths method s representaton and analyss of sgnals n dfferent scales,.e., under dfferent resolutons. The mage s descrbed by a herarchcal structure each level of whch represents the orgnal sgnal wth a certan degree of detal. Tamura et al. [0] presented an approach to descrbng texture on the bass on human vsual percepton. They suggested coarseness, contrast, drectonalty, lne-lkeness, regularty and roughness equvalent to the sx texture propertes that were recognzed as vsually sgnfcant n the course of psychologcal experments. Howarth and Ruger [18, 1] notced that the parameters descrbng the prmary three propertes coarseness, contrast and drectonalty are rather effectve n classfyng and searchng mages by texture. The set of all ponts for one mage s referred to as Tamura mage. Texture analyss by means of the Gabor flters s a specal case of the wavelet approach. Ths s the most frequently used method n mage retreval by texture. In most of the CBIR systems prmarly based n Gabor wavelet [, 3], the mean and standard devaton of the dstrbuton of the wavelet transform coeffcents are used to construct the feature vector. B. Correlaton property Correlaton property shows the lnear dependency of gray level values n the co-occurrence matrx. It presents how a reference pxel s related to ts neghbour, 0 s uncorrelated, 1 s perfectly correlated. Avalable onlne@ 1808

5 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), Correlaton= (4) where μ = C(,) μ = C(,) σ = (-μ ) C(,) σ = (-μ ) C(,) ()C(,)-μμ σσ 5. Dstance Measures Dstance metrcs are consdered among the enqury mage and each mage n the database. Ths procedure s frequent awatng all the mages n the fle have been related wth the query mage. Remoteness among two mages s used to searchng the smlartes between query mage and the mages n the database. Dstance measures lke the cty block, Standard Eucldean dstance method nclude used to found the comparson of feature vectors. In ths paper, we use the Eucldean dstance, Standard Eucldean dstance and also cty block dstance are used to compare the smlarty between the mages. A. Cty-Block dstance (L1) It computes the dstance that may be go to get from one pont to other data pont. The amount of the dssmlarty of ther correspondng example. n (5) 1 d x y B. Eucldean dstance (L) Eucldean dstance s nearly everyone often used to evaluate profles of respondents dagonally varables. Ths s the nearly all commonly-used metrc dstance measure. Eucldean dstance s the rectangle root of the amount of the squared dfferences between equvalent elements of the two vectors. n ( ) (6) 1 d x y 6. Performance Measures Assessment of retreval presentaton s a crtcal trouble n content-based mage retreval (CBIR). Many dfferent methods for measurng the performance of a system have been created and used by researchers. The most common evaluaton measures used n CBIR are precson and recall whch are defned as, Number of relevant mages retreved Precson = Total number of mages retreved 7. Expermental Results Corel mage database of 1000 mages have been used. Each mage s of sze 56x384. There are 10 classes n ths database lke Afrcans, Buldngs, Buses, Dnosaurs, Elephants, Flowers, Mountans and Peoples n database. Each class contans 100 mages. The retreval effcency and effectveness of the proposed texture feature and Dstance measures are expermented wth the popular mage database Corel mage database and the expermental results are presented n ths secton. Ths experment gves the comparson of performance measures of CBIR for the metrc Cty Block dstance (L1), Eucldean dstance (L) and Standard Eucldean Dstance (Std L). Here we compared the GLCM Correlaton property of precson of Class names Buses, Dnosaurs, Elephants, Mountans and peoples. A. Graph Results The graph results n Fg 3. Shows performance analyss related to retreval accuracy of varous class name. C. Standard Eucldean Dstance (Std L) Standardzed Eucldean dstance earnngs Eucldean dstance s planned on regular facts. Standardzed value = (Orgnal value - mean)/standard Devaton n (7) 1 d x y Fg. 3: Precson of Correlaton n Each Class Avalable onlne@ 1809

6 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), The graph results n Fg 4. Shows the average retreval accuracy of varous dstance measures. Fg. 4: Average of Precson Effcency TABLE I Detaled Precson of Correlaton by Class Name Class Name L1 L Std L Buses Dnosaur Elephant Mountan People Average VII. CONCLUSION In ths paper, dscrete wavelet based texture features, assocated wth dfferent dstance measures have been evaluated n Corel data sets. The effcency and performance of the proposed system are measured usng average precson of three dfferent dstance measures. Performance analyss comparson of Correlaton wth dfferent dstance classfer theren one Eucldean dstance gves best performance than cty block and Standard Eucldean dstance. REFERENCES [1] S. Patl and S. Talbar, Content Based Image Retreval Usng Varous Dstance Metrcs, Data Engneerng and Management, Lecture Notes n Computer Scence, Berln Hedelberg: Sprnger, pp , 01, vol [] E. Saber, A.M. Tekalp, Integraton of color, edge and texture features for automatc regon-based mage annotaton and retreval, Journal of Electronc Imagng 7(3), pp , [3] R. Krshnamoorthy, K. Raavayalakshm and R. Pundha, "Low Complexty Hybrd Lossy To Lossless Image Coder Wth Combned Orthogonal Polynomals Transform And Integer Wavelet Transform, ICTACT Journal On Image And Vdeo Processng, Vol., No. 04, pp , May 01. [4] Julen Rechel, Glora Menegaz, Marcus J. Nadenau, and Murat Kunt, Integer Wavelet Transform for Embedded Lossy to Lossless Image Compresson, IEEE Trans. Image Processng, Vol. 10, No. 3, pp , 001. [5] K. Jalaa, C. Bhagvat, B. L. Deekshatulu and Arun K. Puar, Texture Element Feature Characterzatons for CBIR, n IEEE Proc. IGARSS '05, Vol., pp , 005. [6] T. Skora, The MPEG-7 vsual standard for content descrpton an overvew, IEEE Trans. Crcuts Systems and Vdeo Technology, Vol. 11, no. 6, pp , 001. [7] J. Sklansky, Image segmentaton and feature extracton, IEEE Trans. Systems, Man and Cybernetc, Vol.8, no. 4, pp , [8] H. Tamura, S. Mor and T. Yamawak, Texture features correspondng to vsual percepton, IEEE Trans. Systems, Man and Cybernetcs, Vol. 6, no. 4, pp , [9] M. Tuceryan and A. K. Jan, Texture analyss, In the Handbook of Pattern Recognton and Computer Vson, 07-48, [10] F. Lu and R. Pcard, Perodcty, drectonalty and randomness: Wold features for mage modelng and retreval, IEEE Trans. Pattern Analyss and Machne Intellgence, Vol. 18, no. 7, pp , [11] G. Cross and A. Jan, Markov random feld texture models, IEEE Trans. Pattern Analyss and Machne Intellgence, Vol. 5, no.1, pp. 5-39, [1] L. M. Kaplan et al, Fast texture database retreval usng extended fractal features, n Storage and Retreval for Image and Vdeo Databases VI (Seth, I K and Jan, R C, eds), Proc SPIE 331, 1998, pp [13] T. Chang and C.C.J. Kuo, Texture analyss and classfcaton wth tree structured wavelet transform, IEEE Trans. Image Processng, Vol., no. 4, pp , 199. [14] D.S. Zhang., A. Wong., M. Indrawan, and G. Lu, Content-based mage retreval usng gabor texture features, In Proc. of IEEE PCM 00, pp , 000. [15] D. A. Claus and H. Deng, Desgn Based Texture Feature Fuson Usng Gabor Flters and Co- Occurrence Probabltes, IEEE Trans. Image Processng, Vol.14, No. 7, 005. Avalable onlne@ 1810

7 P Mankandaprabhu et al, Int.J.Computer Technology & Applcatons,Vol 5 (5), [16] S. Arvazhagan, L. Ganesan and V. Angayakann, Color Texture Classfcaton usng Wavelet transform, n Proc. of ICCIMA 05, pp , 005. [17] R.M. Haralck, K. Shanmugam, and I. Densten., Textural Features for Image Classfcaton, IEEE Trans. Systems, Man Cybernetcs., vol. 3, no. 6, pp , [18] P. Howarth and S.Ruger, Evaluaton of Texture Features for Content-based Image Retreval, n Proc. of CIVR'04, 004, pp [19] Jayn Kang and Wenuan Zhang, A Framework for Image Retreval wth Hybrd Features, n Proc. of CCDC, 01, pp [0] Tamura, H., Mor, S., and Yamawak, T., Textural Features Correspondng to Vsual Percepton, IEEE Trans. Systems, Man Cybernetcs, vol. 8, pp , [1] Howarth, P. and Ruger, S., Robust Texture Features for Stll Image Retreval, IEEE Proc. Vson, Image Sgnal Processng, vol. 15, no. 6, pp , 005. [] N. Sebe, and M.S. Lew., Wavelet Based Texture Classfcaton, n IEEE Proc. of Int. Conf. on Pattern Recognton, vol. 3, pp , 000. [3] B.S. Manunath, et. al, "Color and texture descrptors", IEEE Trans. Crcuts and Systems for Vdeo Technology, Vol.11(6), pp , 001. [4] Mchael S. Lew, Ncu Sebe, Chabane Deraba and Ramesh Jan, Content based Multmeda Informaton Retreval n State of the Art and Challenge, ACM Multmeda Computng, Communcatons and Applcatons, Vol., No. 1, pp. 1 19, Feb [5] T. Karthkeyan, P. Mankandaprabhu, S. Nthya, A Survey on Text and Content Based Image Retreval System for Image Mnng, Internatonal Journal of Engneerng Research & Technology, Vol. 3 Issue 3, pp , Mar Avalable onlne@ 1811

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