Classification of Product Images in Different Color Models with Customized Kernel for Support Vector Machine

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1 05 Thrd Internatonal Conference on Artfcal Intellgence, lng and Smulaton Classfcaton of Product Images n Dfferent Color s wth Customzed Kernel for Support Vector Machne S.A. Oyewole, O.O. Olugbara, E. Adetba, T. epal ICT and Socety (ICTAS) Reseacrch Group, Durban Unversty of Technology, Durban, South Afrca oyewoledotstanley@gmal.com, oludayoo@dut.ac.za, emmanuela@dut.ac.za, nepal@dut.ac.za Abstract Support Vector Machne (SVM) s wdely recognzed as a potent data mnng technque for solvng supervsed learnng problems. The technque has practcal applcatons n many domans such as e-commerce product classfcaton. However, data sets of large szes n ths applcaton doman often present a negatve repercusson for SVM coverage because ts tranng complexty s hghly dependent on nput sze. Moreover, a sngle kernel may not adequately produce an optmal dvson between product classes, thereby nhbtng ts performance. The lterature recommends usng multple kernels to acheve flexblty n the applcatons of SVM. In addton, color features of product mages have been found to mprove classfcaton performance of a learnng technque, but choosng the rght color model s partcularly challengng because dfferent color models have varyng propertes. In ths paper, we propose color mage classfcaton framework that ntegrates lnear and radal bass functon (LaRBF) kernels for SVM. Experments were performed n fve dfferent color models to valdate the performance of SVM based LaRBF n classfyng 00 classes of e-commerce product mages obtaned from the PI 00 Mcrosoft corpus. Classfcaton accuracy of 83.5% was realzed wth the LaRBF n RGB color model, whch s an mprovement over an exstng method. Keywords - color; gradent; hstogram; mage; kernel; product; vector I. ITRODUCTIO The era of e-commerce has transformed the way retal busness s done and brought consumers much closer to desred products. E-commerce s playng a key role n the global economc growth and ths s why t s mportant to evolve methods to manage the surge of products n ths doman []. One of the most mportant processng tasks n e- commerce s the classfcaton of product mages [], whch nvolves assocatng products to approprate classes. Product classfcaton helps to realze an effcent ndexng and retreval of e-commerce products. It s also useful n advanced technology for n-store shoppng such as shoppng recommendaton assstant system [3]. Image preprocessng, feature extracton and feature classfcaton are essental phases of mage analyss requred for the classfcaton of product mages [4]. The preprocessng of a product mage pror to mage classfcaton s essental n order to mprove qualty of mage data, mprove the computed result, prepare an nput product mage for classfcaton and speed up the processng tme. Commonly used mage pre-processng methods nclude mage reszng, mage segmentaton, nose and background removal. Color s one of the most mportant features usually extracted for product mage classfcaton [5]. The effcacy of color feature resdes n ts unque propertes of beng nsenstve to sze, zoomng and orentaton nvarance under varyng llumnaton condtons. Colors are usually specfed n dfferent color models wth each havng advantages, lmtatons and areas of useful applcatons [5]. Prevous studes have consdered the use of color features n dfferent color models to mprove classfcaton accuracy. Examples of color models commonly employed for product mage classfcaton are RGB, orgb, XYZ, CMYK, YIQ, YUV, YCBCR and HSV [6]. Many machne learnng classfers amongst whch Support Vector Machne (SVM) s wdely recognzed have been extensvely appled for e-commerce product mage classfcaton [, 5, 7, 8]. However, the huge data sets n the e-commerce applcaton doman often nhbt the performance of a sngle kernel for SVM [9, 0]. Moreover, t has been emphaszed that the performance of classfers can be affected for a large number of classes []. The recent developments n the applcaton of SVM have emphaszed the need to consder multple kernel transformaton (MLT) to provde good performance wth greater flexblty for hgh dmensonal data sets [0]. In [9] for nstance, a multple kernels PRBF based on Polynomal (P) kernel and Gaussan Radal Bass Functon (RBF) kernel was used to take advantage of ther respectve strengths. Prevous studes have shown that the number of classes consdered product mage classfcaton s hghly lmted n scope, usually between 3 to 50 classes were used n comparson to the avalable number of product classes n real lfe e-commerce databases [8, ]. In addton, no unversal color model can be used to obtan hgh classfcaton accuracy of product mage data sets acqured from dfferent applcaton domans. Consequently, the motvaton for the study at hand s the need to develop product mage classfcaton systems that can classfy hundreds of product classes wth an acceptable level of accuracy. The frst paramount objectve of ths study s to extract color features of 00 mage classes obtaned from the PI00 database usng the hstogram of orented gradent (HOG) algorthm n fve dfferent color models, whch are RGB, orgb, XYZ, HSV and YUV. The HOG algorthm [3] has been wdely reported n the lterature to be effectve for mage feature extracton. The second mportant objectve of ths study s to /5 $ IEEE DOI 0.09/AIMS

2 develop a customzed kernel based on the lnear kernel and the RBF kernel to mprove the classfcaton of e-commerce product mages usng SVM. The lnear kernel and RBF kernel have been prevously used n solaton for SVM based classfcatons [0]. II. MATERIALS AD METHODS Fg. shows the block dagram of the product mage classfcaton system wth dataset, mage preprocessng, feature extracton and data classfer as components. Fgure. Block dagram of product mage classfcaton wth customzed LaRBF kernel for SVM A. Data sets The product mage utlzed for ths study was obtaned from PI 00 product mage corpus [4]. The PI 00 corpus contans 0000 low resolutons (00 00) mages that are grouped nto 00 classes. Each mage contans the domnant object n relatvely stable forms, exactly the way product mages normally appear on e-commerce webste, such as Amazon.com, Kaymu.com, ebay, Juma.com, Web.com, and Voluson. We chose ths database because t contans colored product mages of good qualtes and t s wdely used for product classfcaton studes [8, ]. Overall, 00 classes of mages wth each class havng 0 color mages were extracted n ths study. Ths culmnates n a total data set of 000 color mages. Fg. shows a sample of selected mages to be Baby shoe, Jacket, Cowboy hat, Can and Brefcase product mage classes. Fgure. Sample of e-commerce product mages from PI 00 B. Image Preprocessng At the preprocessng phase, an nput mage s frst tested to determne f the dmenson s equal to 00 x 00 pxels, otherwse, the mage sze s adjusted to ths dmenson. Thereafter, an nput color mage, whch s n the RGB color model by default s transmtted to the subsequent block or converted nto any of the selected color models such as the orgb, XYZ, HSV and YUV. These color models are brefly descrbed as follows. RGB (Red, Green and Blue) s the most commonly used color model and other color models are usually derved from t by means of lnear or nonlnear transformatons. However, RGB mage s senstve to lumnance, surface orentaton and other photographc condtons. The opponent color model (orgb) [6] s an nvertble transform of RGB and t s prmarly based on the three fundamental psychologcal opponent axes, whch are whte-black, red-green and yellow-blue. Ths color model has three axes, whch are L, C and C channels. The L channel contans the lumnance (luma) nformaton and ts values are between [0, ]. The C and C channels contan the chromatc (chroma) or color nformaton. The values of C and C le wthn [-, ] and [ , ] respectvely. The orgb color model works effcently n computer graphcs and computatonal applcatons. Ths color model has been shown to gve better performance n several mage processng tasks [6]. The transformatons from RGB color model to other color models have been presented. C. Feature Extracton The Hstogram of Orented Gradent (HOG) algorthm was selected for feature extracton n ths study (Fg. ). The dea of HOG s based on the observaton that local object appearance and shape often can be well characterzed by the dstrbuton of local ntensty gradents. For each pxel n an mage, HOG computes the gradent magntude at dfferent orentaton and then bulds a hstogram representaton of the entre mage. HOG s one of the most powerful feature descrptors n the mage processng lterature. In ths study, we followed the same prncple appled n Dalal et al [3] to compute the HOG features from the preprocessed color mages n each channel of the selected color models. Each product mage s dvded nto smaller rectangular block of 9 x 9 pxels and each block s further dvded nto 9 cells of 3 x 3 pxels. We used gradent orentaton of 9 bns rangng from Fnally, we combned all the gradent orentaton hstograms n the three color channels to form a feature vector of sze 43 (9 bns x (3 x 3) pxels x 3 channels) for one product mage. D. Feature Classfcaton The SVM s a wdely used supervsed learnng algorthm for solvng dverse classfcaton problems [8, 5, 6]. It has been successfully employed n varous applcaton domans such as bometrcs and bonformatcs [7], text categorzaton [7] and product mage classfcaton [8]. The 54

3 classfer dscrmnates mage samples of dfferent classes by tryng to fnd a separatng hyperplane wth the maxmum margn n the nput space [8]. In a bnary classfcaton task, gven a tranng data set {(, )} = of x y,..., x x and class nstances consstng of an mage feature labels y {, } [5], a lnear decson surface can be defned as: w. x + b = 0 () The tranng of SVM s to fnd w R and b that maxmze the margn between the two classes. The decson surface of an SVM can be obtaned by solvng a constraned optmzaton problem that can be stated as: mnw w Subject to the constrant: y ( wx. +b) () The optmzaton problem does not have a soluton f the data ponts are not lnearly separable. To handle nonlnearly separable problems, whch s the case n ths study, the modfed SVM ntroduces a slack varable ξ 0 and the modfed optmzaton problem can be stated as: mn w, ξ w + C ξ Subject to: y ( w. x + b) ξ, =,..., (3) where C > 0 s a regularzaton parameter controllng the penalty for a msclassfcaton. Snce the objectve functon w s convex, mnmzng t under the lnear constrant n Equaton (3) can be acheved usng the Lagrange multplers [5]. If we denote the non negatve Lagrange multplers assocated wth the lnear constrant n Equaton (3) by α = ( α,..., α ), the optmzaton problem leads to maxmzng: (4) w( α) = α α α j y y j x x j = j wth α 0 and under the constrant: =. y α = 0 Once the vector α = α,..., α ),whch s a soluton of the ( maxmzaton problem n Equaton (4) s obtaned, the optmal separatng hyperplane can be represented as: w = α y x (5) = The hyperplane decson functon can be defned as: f ( x) = sgnα y x x + b = The non-zero tranng feature vectors (6) α are called the support vectors. In addton, complex nonlnear boundares are ntroduced n the orgnal nput space by SVM usng kernels. A kernel must satsfy the condton of Mercer [9]. Examples of the SVM kernels that satsfy the condton of Mercer are lnear, polynomal, radal bass functon, quadratc and Cauchy. Wth these kernels, Equaton (6) becomes: (7) f ( x) = sgnα yk( x x) + b = E. Proposed LaRBF Kernel The choce of kernels often determnes the performance of SVM. Based on the shortcomngs of a sngle kernel for SVM [0,, 9], lterature has suggested multple kernels to mprove classfcaton performance [7, 0]. Although, multple kernels have been used n SVM for classfcaton n e-commerce [7, 8, 0,, 0], they are mostly appled to a lmted number of classes, whch are far from the practcal realty n ths applcaton doman. Dfferent approaches for combnng kernels for SVM can be appostely grouped nto: lnear, non-lnear and datadependent [8]. The lnear combnaton approach s the smplest of all and t s further categorzed nto weghted sum and unweghted sum or smply an addtve combnaton. The unweghted sum lnear combnaton approach has very low computatonal cost [8], whch s the reason for usng t n ths study to realze the LaRBF kernel. The LaRBF kernel possesses the ntrnsc benefts of each component kernel. These nclude the RBF hgh classfcaton accuracy and the ntrnsc hgh speed performance n lnear kernel [0]. The lnear kernel s represented as: K Lnear ( x, = x. y (8) The RBF kernel s represented as: x y (9) K RBF ( x, = exp σ The proposed LaRBF multple kernel for SVM s consequently obtaned from Equatons (8) and (9) as: LaRBF = K ( x, K ( x, (0) Lnear + where σ s a bandwdth parameter of the RBF kernel. The LaRBF multple kernel s an admssble Mercer s kernel because both of ts lnear and RBF consttuent kernels satsfy the condton of Mercer. Ths s premsed on the fact that lnear combnaton of Mercer s kernels s also Mercer complant [9]. Ths new kernel apples to the classfcaton of product mages n ths study and t s expermentally compared wth four other kernels as detaled n the subsequent secton. F. Experments All the experments n ths paper were performed on a PC wth Intel Core speed wth 4.00GB RAM and 64-bt Wndows 7 operatng system. To carry out the necessary experments, the pre-processng procedure, whch nvolves the transformaton of the RGB color model to the selected color models, was mplemented RBF 55

4 n the MATLAB R0a programmng envronment. Smlarly, ths programmng platform was used to mplement all the algorthms dscussed n ths paper, such as the HOG algorthm for feature extracton and the proposed LaRBF for SVM. We utlzed the One-Aganst-All (AA) strategy to extend the SVM to the case of the multple class classfcaton task n ths study. In addton, 0-fold cross valdaton was used to tran each of the SVM confguratons and for the RBF kernel for SVM, the bandwdth parameter 3 s σ =. Fve dfferent experments were performed n ths study to determne the performance of product mage classfcaton usng the extracted HOG features n the RGB, orgb, XYZ, HSV and YUV color models respectvely. One experment s performed for each color model and for each experment, we compared three sngle kernels, whch are lnear, polynomal and radal bass functon as well as two multple kernels, whch are the PRBF [7] and the proposed LaRBF. Moreover, fve expermental trals were carred out for each kernel of the SVM and the average classfcaton accuracy was thereafter computed. III. RESULTS AD DISCUSSIO The HOG features for the frst experment were extracted n the RGB color model. The results of the fve dfferent trals for each of the kernels n the frst experment are shown n Table I. As shown n the Table, the hghest average accuracy of 83.5% s obtaned wth the proposed LaRBF kernel. Ths accuracy s better than what was obtaned from lnear and RBF kernels, whch are the sngle kernel components of the LaRBF. The accuracy of the proposed LaRBF s also better than PRBF multple kernel. The lower accuracy of the PRBF multple kernel, s as a result of the poor performance of a sngle polynomal kernel, whch s one of ts components. Ths result shows the superorty of LaRBF n the RGB color model. TABLE I: CLASSIFICATIO RESULTS OF SVM KERELS I RGB COLOR MODEL SVM Kernels n RGB Color Classfcaton Accuracy (%) Tral S/ Kernel Lnear Polynomal RBF PRBF LaRBF In the second experment, the HOG features were extracted n the orgb color model. The result of ths experment s shown n Table II. As shown n the Table, the hghest average accuracy of 8.5% was obtaned wth the proposed LaRBF kernel. Smlar to the result n the frst experment, the proposed LaRBF kernel gves a better classfcaton accuracy than when lnear kernel, RBF kernel and PRBF multple kernel were used. However, the classfcaton accuracy of the proposed LaRBF kernel s better n the RGB color model n the frst experment than the orgb color model n the second experment. TABLE II: RESULTS OF SVM KERELS CLASSIFICATIO I ORGB COLOR MODEL SVM Kernels n orgb Color Classfcaton Accuracy (%) Tral S/ Kernel Lnear Polynomal RBF PRBF LaRBF The HOG features for the thrd experment were extracted n the HSV color model. The result of the experment s shown n Table III. The hghest accuracy of 7.7% was obtaned usng the proposed LaRBF kernel. Ths result s better than the lnear kernel (70.3%), RBF kernel (67.%) and the PRBF multple kernel (70.3%). However, the average accuracy obtaned n the HSV color model n ths experment s poorer than the result n the frst and the second experment n whch RGB and orgb color models were utlzed respectvely. TABLE III: CLASSIFICATIO RESULTS OF SVM KERELS I HSV COLOR MODEL SVM Kernels n Classfcaton Accuracy (%) HSV Tral S/ Kernel Lnear Polynomal RBF PRBF LaRBF In the fourth experment, the feature extracton was done n the XYZ color model. The results of ths experment are shown n Table IV. As shown n the Table. The hghest accuracy of 80.5% was obtaned wth the proposed LaRBF kernel. Smlar to the results n the three prevous experments, the proposed LaRBF kernel outperformed the lnear (80.0%) and RBF (78.8%) kernel. It also outperformed the PRBF multple kernel. whle, the LaRBF accuracy n the XYZ color model n the current experment s poor compared to ts accuracy n the RGB and orgb color models. The accuracy of the LaRBF kernel s however better n the XYZ color model than n the HSV color model. 56

5 TABLE IV: CLASSIFICATIO RESULTS OF SVM KERELS I XYZ COLOR MODEL SVM Kernels n XYZ Color Classfcaton Accuracy (%) Tral In the ffth experment, whch s the last n ths study, the HOG features were extracted n the YUV color model. The results of ths experment are shown n Table V. As shown n the Table, the proposed LaRBF kernel outperforms all the other kernels. Ths result s smlar to the results obtaned n the four prevous experments. The average accuracy of 80.5% for LaRBF n the YUV color model s the same as was obtaned usng the XYZ color model n the fourth experment. The classfcaton accuracy of the LaRBF kernel n the YUV color model s better than was obtaned for HSV n the thrd experment. However, the results n the frst and second experments n the RGB and orgb color models are better than the result obtaned for the YUV color model. TABLE V: CLASSIFICATIO RESULTS OF SVM KERELS I YUV COLOR MODEL The results of the fve experments, whch are summarzed and plotted n Fg. 3, show that the proposed multple kernels LaRBF-SVM gave the best average classfcaton accuracy of 83.5% n the RGB color model. Ths s followed by the average accuracy obtaned for the orgb color model. It can also be observed that all the kernels consdered n ths study (except the polynomal kernel) gave ther hghest accuraces n the RGB color model. Fgure 3. Summary of average classfcaton accuracy S/ Kernel Lnear Polynomal RBF PRBF LaRBF SVM Kernels n YUV Color Classfcaton Accuracy (%) Tral S/ Kernel Lnear Polynomal RBF PRBF LaRBF The results of ths study show that LaRBF kernel consstently outperformed the lnear and RBF kernels used n solaton. Ths perfectly agrees wth the lterature result that multple kernel SVM usually gve better performance than sngle kernel SVM [0, 0]. The results obtaned n ths study also show that the proposed multple kernel LaRBF outperformed a smlar multple kernel PRBF n all the experments. Although the author n [9] acheved 9.5% accuracy for a data set of 6 classes usng the PRBF multple kernel, t dd not acheve such level of accuracy n our expermental results. Ths s because of the large sze of our data set, whch contans 00 classes and the poor performance of the polynomal kernel, whch has been reported to have poor capablty for hgh dmensonal data set. The expermental results n ths study provde a strong valdaton of the effcacy of the proposed LaRBF multple kernels. So far, we have been able to acheve the two overarchng objectves of ths study, whch are to use the HOG algorthm to extract a set of color features from 00 classes of mages n the PI 00 corpus and to develop a multple kernel from the lnear kernel and RBF kernel to mprove the accuracy of product mage classfcaton. IV. COCLUSIO In ths paper, we proposed customzed LaRBF kernel based on lnear and RBF kernels. The HOG features extracted n fve dfferent color models were used to tran an SVM that utlzes four exstng kernels and the proposed LaRBF kernel. Expermental results show that the proposed LaRBF kernel based SVM gave the best average accuracy n the RGB color model. Ths result s hghly promsng for desgnng practcal applcatons of product mage classfcaton, recommendaton and other e-commerce systems. We hope to mprove the performance obtaned n ths study n the future by ncorporatng color mage segmentaton at the pre-processng phase. REFERECES [] S. Bergamasch, F. Guerra and M. Vncn, Product Classfcaton Integraton for E-commerce, Proceedngs of the 3th Internatonal Workshop on Database and Expert Systems Applcatons (DEXA 0), vol., 00, pp /0. [] A. Kannan, P. P. Talukdar,. Raswasa, and Q. Ke, Improvng Product Classfcaton Usng Images, In ICDM, 0, pp [3] O.O. Olugbara, S.O. Ojo and M.I. Mphahlele, Explotng Image Content n Locaton-Based Shoppng Recommender Systems for Moble Users, Internatonal Journal of Informaton Technology and Decson Makng, (World Scentfc Publshng Compan, vol. 9(5), 00, pp [4] E. Elharr,. Bendary, M. M. M. Fouad, J. Platos, A. Ella Hassanen, and A. M.M. Hussen, Mult-class SVM Based Classfcaton Approach for Tomato Rpeness, A. Abraham et al. (eds.), Innovatons n Bo-nspred Computng and Applcatons, 75 Advances n Intellgent Systems and Computng (Sprnger), 04, pp [5] S. Banerj, A. Verma, C. Lu, ovel Colour LBP Descrptors for Scene and Image Texture Classfcaton, In : 5 th nternatonal 57

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