STUDIES ON IMPROVING TEXTURE SEGMENTATION PERFORMANCE USING GENERALIZED GAUSSIAN MIXTURE MODEL INTEGRATING DCT AND LBP

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1 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: STUDIES ON IMPROVING TEXTURE SEGMENTATION PERFORMANCE USING GENERALIZED GAUSSIAN MIXTURE MODEL INTEGRATING DCT AND LBP K. NAVEEN KUMAR, 2 K.SRINIVASA RAO, 3 Y.SRINIVAS, 4 CH. SATYANARAYANA Dept. of IT, GITAM Univesity, Visakhapatnam 2 Dept of Statistics, Andha Univesity, Visakhapatnam 3 Dept. of IT, GITAM Univesity, Visakhapatnam 4 Dept of CSE, JNTUK-Kakinada, Kakinada nkumakuppili@gmail.com, 2 ksaoau@yahoo.co.in, 3 dys@gitam.edu, 4 chsatyanaayana@yahoo.com ABSTRACT This pape addesses the pefomance evaluation of the textue segmentation integating DCT with LBP. In this method, the whole image is conveted in to local binay patten domain. The LBP image is then divided into diffeent non ovelapping blocks. Fom each block, the DCT coefficients ae selected in a zigzag patten fo each block. Assuming the featue vectos follow a multivaiate genealized Gaussian mixtue model, the model paametes ae estimated using EM algoithm. The initialisation of the model paametes is caied using moment method of estimation and using Hieachical clusteing algoithm. The textue segmentation algoithm is developed unde Bayesian fame with component maximum likelihood. The pefomance of the poposed algoithm is evaluated using pefomance measues such as GCE, PRI and VOI with andomly selected images fom Bodatz database. It is obseved that this algoithm outpefoms existing textue segmentation algoithms with espect to pefomance measues. Keywods: Textue Segmentation, Multivaiate Genealized Gaussian Mixtue Model, Pefomance Measues, Local Binay Pattens, DCT Coefficients.. INTRODUCTION Textue segmentation is one of the most impotant consideation fo image analysis, Hence seveal methods developed on vaious methods fo textue segmentation[-2]. Haim Pemute et.al, (2006)[3] has pesented a eview on textue classification using diffeent appoaches such as suppot vecto mechanisms, histogam with maps, vecto quantization, neaest neighbou classification, Fissue tansfomation, Makov andom fields and pobability models. Geman and Gaffigne (987)[4] has povided a eview on Mako andom fields fo textue analysis. Pemute et.al.(2003)[5] has utilised the Gaussian mixtue model fo textue classification and shown that this method impoved the pefomance ove othe methods, But still, the textue segmentation method based on Gaussian mixtue model ae lagging behind the standad citeia of segmentation pefomance measues such as coect ate. Accoding to Pemute et.al,(2005)[3], the classification pefomance of textue segmentation algoithm based on Gaussian mixtue model is only 85.2%. The efficiency of the textue segmentation pefomance is impoved futhe by consideing two impotant aspects namely the featue vecto extaction as well as descibing the suitable model to the featue vecto. Recently, Naveen Kuma et.al, (205)[6-7] have consideed textue segmentation using DCT coefficients and genealized Gaussian mixtue model. These methods impoved the pefomance ove that of Gaussian mixtue model. But still thee is a gap, one has to look the dawbacks of the featue vecto extaction using DCT coefficients only[8]. The majo dawback of consideing DCT coefficients fo extacting featue vecto is taking educed (few) DCT coefficients in each block. These DCT coefficients will povide the global (maco) infomation of the image. But they may miss the vey cucial local infomation such as connectivity 200

Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No.2 2005-206 JATIT & LLS. All ights eseved. ISSN: 992-8645 www.jatit.

The LBP is capable of captuing local infomation moe accuately (D. Haitha et.al(202))[9].

2 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: between adjacent pixels in the image. Theefoe to have an accuate textue segmentation one has to conside the integation of DCT coefficients with local binay pattens. The LBP is capable of captuing local infomation moe accuately (D. Haitha et.al(202))[9]. Hence in this pape we develop a textue segmentation method based on multivaiate genealized Gaussian mixtue model using integation of DCT with LBP. The est of the pape is pesented as follows. Section deals with the featue vecto extaction using DCT coefficients and local binay pattens. Section 3 is concened with image textue model using multivaiate genealized Gaussian mixtue model. Section 4 deals with estimation of model paametes using EM algoithm. Section 5 is concened initialisation of model paametes. Section 6 deals with expeimentation with poposed algoithm. Section 7 pesents the pefomance evaluation metics and compaative study with ealie models. Section 8 deals with conclusions. 2. FEATURE VECTOR EXTRACTION USING DCT COEFFICIENTS AND LOCAL BINARY PATTERS The discete cosine tansfom is capable of denoising the image and LBP captues the mico level infomation of the textue featues. The integation of DCT coefficients and local binay pattens will povide efficient extaction of featue vecto that can be incopoated in Textue segmentation pocess. The local binay patten opeato convets each pixel intensity with a decimal numbe by captuing local stuctue aound each pixel. By subtacting the cente pixel value fo each pixel is compaed with its neighbouhood eight pixel values. The negative values ae coded with zeo and all othes values with esulting a binay numbe in clockwise otation. The pocess usually stats fom top left neighbou and the associated decimal values that ae geneated by concatenating binay values ae called local binay pattens(lbp) (Huang et al.,(20)[0] and Chi et al.,(2007)[]). The basic dawback of LBP opeato is that its small 3X3 neighbouhood cannot captue dominant featues with lage scale stuctues. To deal with textue at diffeent scales, the opeato was genealized to use neighbouhoods of diffeent sizes (Ojala et al., (2004)[2]). Given a pixel at (x c,y c ), the esulting LBP is expessed in decimal fom as LBP P,R (x c,y c ) = p p= 0 q( i i)2 p () whee, i c coesponds to the gay value of the cente pixel (x c,y c ), i p efes to gay values of P equally spaced pixels on a cicle of adius R, and s defines a thesholding function as, if x 0 q(x)= 0, othewise The basic LBP opeato is invaiant to monotonic gay-scale tansfomations, which peseve pixel intensity ode on the local neighbouhoods. The opeato LBP (P,R) poduces 2 P diffeent output values, coesponding to 2 P diffeent binay pattens fomed by P pixels in the neighbouhood. The DCT featues extacted fom LBP-images have lowe atio than the ones extacted fom pixel intensity values. Theefoe, the featue vecto extaction is implemented in two methods namely, DCT+LBP and DCT+LBP unde logaithmic domain. In the fist method, the image is tansfomed in to LBP domain. The obtained LBP image is then divided into non ovelapping blocks of MxN size. Figue : Oiginal Textue Images And Images Unde LBP Domain Fo each block, the 2D DCT s coefficients ae extacted afte odeing them in zig-zag fashion. The obtained coefficients fom the featue vecto set. In the othe method, to compensate the illumination vaiations, log DCT s ae consideed as small vaiation of the local featues epesenting textue pattens known as mico level infomation has significant influence. To have efficient featue extaction, the appoach namely, utilizing the maco and mico level infomation s unde ideal conditions ae to be integated, esulting in a geneic appoach. The steps fo featue vecto extaction is conveting the image in to LBP domain. The conveted image is divided into blocks and DCT coefficients ae obtained afte odeing them in zig-zag fashion epesenting the featue vecto set fo textue segmentation. c p 20

3 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: MULTIVARIATE GENERALIZED GAUSSIAN MIXTURE MODEL In textue analysis, the entie image textue is consideed as a union of seveal epetitive pattens. In this section, we biefly discuss the pobability distibution (model) used fo chaacteizing the featue vecto of the textue. The featue vecto chaacteizing the image is to follow M-component mixtue distibution. Theefoe we develop and analyze the textues in an image by consideing that the featue vectos epesenting textues follow M-component multivaiate genealized Gaussian mixtue distibution (MGGMM) model[4]. The joint pobability density function of the featue vecto associated with each individual textue is p(x / θ ) = (2) whee, x = (x ), j=,2,..d, is D dimensional andom vecto epesents the featue vecto.i =,2,.M epesenting the goups, =,2,.T epesenting the samples. θis a paametic set such that θ = ( µ, σ, ), w i is the component weight M such that wi = and gi ( x, θ) is the i= pobability of i th class epesenting by the featue vectos of the image and the D-dimensional genealized Gaussian distibution is of the fom [3]. D K( ) x µ g(x / θ ) = exp ( A( ) 2σ j= σ (3) whee,,, paametes. M w g x µ σ ae location, scale and shape /2 Γ(3/ ) (/ ) K( ) Γ and = Γ (/ ) ( θ) i i, i= /2 Γ(3/ ) A( ) = Γ(/ ) (4) with Γ() denoting gamma function. Each paamete 0 contols the shape of GGD. This implies, D x µ g(x / θ ) = exp 2 A(, ) j= A(, σ) Γ ( + ) σ (5) The mean value of the genealized Gaussian distibution is x µ A(, σ) E(x ) = xe dx 2 Γ ( + )A(, σ) = µ (6) The GGD is symmetic with espect to µ, hence the odd cente moments ae zeo i.e., t Ex µ = 0,t =,3,5,. The even cente moments can be obtained fom absolute cente moments and given by /2 2 t + σγ Γ t E x µ = 3 Γ Γ The vaiance is va (x) = E(x x) = E(x µ ) =σ (7) (8) The model can have one covaiance matix fo a genealized Gaussian density of the class. The covaiance matix Σ can be full o diagonal. In this pape, the diagonal covaiance matix ix consideed. As a esult of diagonal covaiance matix fo the featue vecto, the featues ae independent and the pobability density function of the featue vecto is x exp µ D A(, σ) (9) g i(x / θ ) = j= 2Γ + A(, σ) 4. ESTIMATION OF MODEL PARAMETERS USING EM ALGORITHM In this section, we conside estimation of model paametes using EM algoithm that maximizes the likelihood function of the model [4]. The sample obsevations (DCT Coefficients) (x,x,...x ) 2 ae dawn fom image textue which is chaacteized by the joint pobability density function M p(x / θ ) = wigi ( x, θ) i= (0) 202

4 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: whee, gi ( x, θ) (9). is given in the above equation To find the efined estimates of paametes w, µ and σ fo i=,2,3, M; j=,2,..,d. we i maximize the expected value likelihood o log likelihood function. The shape paamete is estimated using the pocedue given by ShaoquanYU(202) [5]. w, µ and σ, we use the EM To estimate i algoithm which consists of two steps namely, Expectation (E ) Step whee we estimate initial w, µ and σ fom a given textue paametes i image data and Maximization (M) Step is to (l) maximizeq( θ, θ ). Using the steps in the EM algoithm, we get the updated equations fo the paametes as shown below. T (l) (l) (l+ ) w i.g i(x, θ ) wi = M T (l) (l) = w i i.g i(x, θ ) = () (l) (l) (l) θ = µ, σ ae the estimates at i th whee ( ) iteation. T (l) A(N, ) t i(x, θ ) (x t) (l+ ) µ = = T (l) A(N, ) t i(x, θ ) = whee, A(N, ) is some function = fo and must be equal to fo of N=2, we have also obseved that must be inceasing function of. 3 Γ T (l) t i(x, ) θ.x µ = Γ σ (l+ ) = T (l) t i(x, θ ) = (2) = 2, in the case A(N, ) (3) 5. INITIALIZATION OF MODEL PARAMETERS The efficiency of the EM algoithm in estimating the paametes is heavily dependent on the numbe of goups and the initial estimates of w, µ and σ fo the model paametes i i=,2,3, ; j=,2,,d. Usually in EM algoithm, the mixing paamete w i and the distibution µ and σ ae given with some initial paametes values. A commonly used method in initialization is by dawing a andom sample fom the entie data. To utilize the EM algoithm, we have to initialize the paametes which ae usually consideed as known apioi. The initial value of wican be taken as w i=/m, whee M is the numbe of textue image egions obtained fom Hieachical clusteing algoithm. Then we obtain the initial estimates of the paametes though sample moments as w i = /M σ = Standad Deviation of M th Class µ = T x T = substituting these values as the initial estimates, the efined estimates of the paametes can be obtained using EM Algoithm by simultaneously solving the equations (), (2) and (3) using MATLAB envionment. 6. EXPERIMENTATION WITH PROPOSED SEGMENTATION ALGORITHM The segmentation algoithm involves the following steps. Step : The featue vectos ae obtained by using the technique discussed in section 2. Step 2: The samples ae divided into M goups by Hieachical clusteing Algoithm[6]. Step 3: The mean vecto, vaiance vecto, µ and σ fo each class of the multivaiate data is computed. Step 4: Take w i = /M, fo i=,2,3,..m. Step 5: The efined estimates of w i, each class ae obtained using the updated equations of the EM algoithm. µ and σ fo 203

x That is, Featue vecto t is assigned to the j th egion fo which L is maximum. j whee, L j x µ exp D A( ρ, σ ) = max j = 2 A(, σ ) Γ + 7.

5 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: Step 6: The assignment of each featue vecto into the coesponding j th egion (segment) is pefomed accoding to the maximum likelihood of the j th component L j. x That is, Featue vecto t is assigned to the j th egion fo which L is maximum. j whee, L j x µ exp D A( ρ, σ ) = max j = 2 A(, σ ) Γ + 7. PERFORMANCE EVALUATION AND COMPARATIVE STUDY (4) To assess the ability and pefomance of the developed model, textue segmentation is to be pefomed by using the benchmak dataset of textues available in the Bodatz Textue databases[7]. Fo each textue image, Hieachical algoithm is employed ove the data of featue vectos that ae divided in to M goups. The initial µ and σ ae estimate of the paametes w i, obtained fo each goup using heuistics clusteing and moment estimatos. Using these initial estimates, the efined estimates ae calculated based on the updated equations obtained though EM Algoithm. With these values, textue segmentation is pefomed based on likelihood of data belonging to a paticula goup. Then the segmentation image is dawn fo the poposed algoithm. The segmentation and quality metics ae evaluated fo the poposed model. The image segmentation pefomance measues namely; Pobabilistic Rand Index (PRI), the Vaiation of Infomation (VOI) and Global Consistency Eo (GCE) ae computed fo the poposed method. The Rand index given by Unnikishnan et al (2007)[8] counts the faction of pais of pixels whose labeling ae consistent between the computed segmentation and the gound tuth. This quantitative measue is easily extended to the Pobabilistic Rand index (PRI). The vaiation of infomation (VOI) metic given by Meila (2007)[9] is based on elationship between a point and its cluste. It uses mutual infomation metic and entopy to appoximate the distance between two clusteing s acoss the lattice of possible clusteing s. It measues the amount of infomation that is lost o gained in changing fom one clusteing to anothe. The Global Consistency Eo (GCE) given by Matin D. et al (200)[20] measues the extent to which one segmentation map can be viewed as a efinement of segmentation. Fo a pefect match, evey egion in one of the segmentations must be identical to, o a efinement (i.e., a subset) of, a egion in the othe segmentation. The image segmentation pefomance measues namely, PRI,GCE,VOI ae computed fo the five images with espect to the developed model, Genealized Gaussian Mixtue Model with k- Means and Hieachical algoithm to that of othe models. Figue 2: Oiginal And Segmented Textue Images With LBP Featues 204

Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No.2 2005-206 JATIT & LLS.

og E-ISSN: 87-395 Table : Segmentation Pefomance Measues Of The Textued Images multivaiate genealized

The accuacy of the classifie is also studied fo the sample images by using confusion matix fo segmented

Table 3 shows the values of accuacy, Sensitivity,Specificity,Pecision, Recall,F-Measue fo the segmented

Fom Table it is obseved that the segmentation pefomance measues of the poposed segmentation algoithm ae

Table 2 pesents the miss classification ate of the pixels of the sample using the poposed model and ealie

Table 2: Miss Classification Rate Of The Classifie Model MGGMM-H and log DCT MGGMM-H and DCT +LBP MGGMM-H

ate of the classifie with the Table 3: Compaative Study Of MGGMM Withhieachical Clusteing Algoithmwith

classifie is moe than the ealie Gaussian mixtue models.

6 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: Table : Segmentation Pefomance Measues Of The Textued Images multivaiate genealized Gaussian mixtue model is less when compaed to that of GMM. The accuacy of the classifie is also studied fo the sample images by using confusion matix fo segmented egions and computing the quality metics [2].Table 3 shows the values of accuacy, Sensitivity,Specificity,Pecision, Recall,F-Measue fo the segmented egions in the image textue. Fom Table it is obseved that the segmentation pefomance measues of the poposed segmentation algoithm ae close to the optimal values of PRI, GCE and VOI. Table 2 pesents the miss classification ate of the pixels of the sample using the poposed model and ealie Gaussian mixtue model. Table 2: Miss Classification Rate Of The Classifie Model MGGMM-H and log DCT MGGMM-H and DCT +LBP MGGMM-H and log DCT+LBP Miss-classification Rate 2% 0% 9% Fom the Table 2, it is obseved that the misclassification ate of the classifie with the Table 3: Compaative Study Of MGGMM Withhieachical Clusteing Algoithmwith Vaiants Of DCT, LBP And Log DCT Fom Table 3, it is obseved that the F- measue value fo the poposed classifie is moe than the ealie Gaussian mixtue models. This indicates that the poposed classifie pefom well than that of Gaussian mixtue model. 8. CONCLUSIONS A textue segmentation algoithm based on multivaiate genealized Gaussian mixtue model integated with DCT and LBP is developed and analyzed. The DCT and log DCT coefficients ae capable of chaacteizing the maco infomation of the textues keeping in view the illumination compensation due to diffeent lightning and envionmental conditions. The textue of the image is modeled using multivaiate genealized Gaussian mixtue model. The input image is fist tansfomed in to LBP domain to captue mico 205

7 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: infomation and LBP image is consideed fo featue vecto extaction. The LBP image is divided in to blocks of non ovelapping egions. Fo each block, the DCT coefficients ae computed and ae selected in zig0zag patten. The obtained DCT coefficients fom the featue vecto fo textue segmentation. The EM algoithm is consideed and initial paametes ae obtained using Hieachical clusteing algoithm and moment method of estimation. The updated paametes ae computed and segmentation is pefomed based on maximum likelihood unde Bayesian fame. The pefomance of the segmentation algoithm is evaluated by consideing five andom images chosen fom Bodatz textue database. It is obseved that the model using DCT and LBP pefoms bette than ealie methods based on DCT and DCT unde logaithmic domain. It is also obseved that the pefomance metics namely GCE,PRI,VOI,F-measue ae bette than ealie models. It is possible to extend this algoithm using tuncated genealized Gaussian distibution with DCT and LBP which will be taken elsewhee. REFERENCES: [] Pal S.K and Pal N.R. (993), A eview on Image Segmentation Techniques, IEEE Tans. on Patten Recognition, Volume 26(9), pp [2] Vaainath V. Bhosle, VushsenP.Pawa (203), Textue Segmentation: Diffeent Methods, Intenational Jounal of Soft Computing and Engineeing, Vol.3(5), pp [3] Pemute Haim, Joseph Fancos and Ian Jemyn (2006), A study of Gaussian mixtue models of colo and textue featues fo image classification and segmentation, Patten ecognition Society, Vol.39, pp [4] Geman. S and Gaffigne.C ((987), Makov andom field image models and thei applications to compute vision, Poceedings of Intl. Congess of Mathematicians 986, Ameican Mathematical Society, pp [5] Pemute Haim, Joseph Fancos and Ian Jemyn (2003), Gaussian mixtue models of textue and colou fo image database etieval, Poceedings of IEEE Intl. Confeence on Acoustics, Speech and Signal pocessing, Vol.3,pp [6] Naveen Kuma.K, Sinivasa Rao.K, Sinivas.Y and Satyanaayana. Ch(205), Textue Segmentation based on multivaiate genealized Gaussian mixtue model,cmes Jounal, Vol.07(3),pp [7] Naveen Kuma. K, Sinivasa Rao.K, Sinivas.Y and Satyanaayana. Ch(205), Textue Segmentation using multivaiate genealized Gaussian mixtue model unde log DCT domain, Intenational Jounal of Applied Engineeing Reseach, Vol.0(22), pp [8] Rao K.R. and Yip P. (990), Discete Cosine Tansfom Algoithms, Advantages, Applications, Academic pess, New yok, USA [9] Haitha.D, Sinivasa Rao.K, Sinivas.Y and Satyanaayana. Ch(202), Pefomance evaluation on the effect of combining DCT and LBP on Face ecognition system, Intenational Jounal of Moden Education and Compute Science, Vol.4(),pp [0] Huang Di., Yunhong Wang(20), Local Binay Pattens and its applications to facial Image Analysis: A suvey, IEEE tans. On Sys. Man and Cybe., Vol.4(6),pp [] Chi Ho Chan, Josef Kittle and Kieon Messe(2007), Multi scale local binay patten histogam fo face ecognition, Intl. Jounal of Advances in Biometics, Vol.4642(9),pp [2] OjalaT.,Pietikainen.M and Maenpaa.T (2004), Multiesolution gay-scale and otation invaiant textue classification with local binay pattens, IEEE Tans. on Patten Anal. and Machine Intel., Vol.24(7), pp [3] M.S.Allili and NizaBougila (2008), Finite genealized Gaussian mixtue modeling and applications to image and video foegound segmentation, Jounal of Electonic Imaging, Vol. 7(3), pp.05-3 [4] Mclanchlan G. and Peel D.(2000), The EM Algoithm Fo Paamete Estimations, John Wileyand Sons, New Yok [5] Shaoquan YU, Anyi Zhang, Hongwei LI (202), A Review on estimating the Shape Paametes of Genealized Gaussian Distibution, Jounal of Infomation Systems, Volume 8(2),pp [6] Sinivas Y. and SinivasRao K. (2007), Unsupevised image segmentation using finite doubly tuncated Gaussian mixtue model and Hieachical clusteing, Jounal of Cuent Science,Vol.93(4), pp [7] P. Bodatz, Textue: a photogaphic album fo atists and designes, Dove, New Yok, 206

8 Jounal of Theoetical and Applied Infomation Technology 3 st Octobe 206. Vol.92. No JATIT & LLS. All ights eseved. ISSN: E-ISSN: ( [8] Unnikishnan R., Pantofau. C., and Henbet M. (2007), Towad objective evaluation of image segmentation algoithms, IEEE Tansactions in Patten Annl.&Mach.Intell,Vol.29(6), pp [9] M. Meila (2007), Compaing clusteings- an infomation based distance, Jounal of Multivaiate Analysis, Vol. 98,pp [20] D. Matin, C. Fowlkes, D. Tal and J. Malik (200), "A Database of Human Segmented Natual Images and its Application to Evaluating Segmentation Algoithms and Measuing Ecological Statistics", Poc. 8th Int l Conf. Compute Vision, Vol. 2, pp [2] Powes, David M.W. (20), Evaluation: Fom Pecision, Recall and F-Measue to ROC, Infomedness, Makedness and Coelation, Jounal of Machine Leaning Technologies, Vol.2(),pp

Journal of World s Electrical Engineering and Technology J. World. Elect. Eng. Tech. 1(1): 12-16, 2012

Journal of World s Electrical Engineering and Technology J. World. Elect. Eng. Tech. 1(1): 12-16, 2012 2011, Scienceline Publication www.science-line.com Jounal of Wold s Electical Engineeing and Technology J. Wold. Elect. Eng. Tech. 1(1): 12-16, 2012 JWEET An Efficient Algoithm fo Lip Segmentation in Colo