A Novel Feature Extraction Algorithm for Haar Local Binary Pattern Texture Based on Human Vision System

Transcription

1 A Novel Feature Extractio Algorithm for Haar Local Biary Patter Texture Based o Huma Visio System Liu Tao 1,* 1 Departmet of Electroic Egieerig Shaaxi Eergy Istitute Xiayag, Shaaxi, Chia Abstract The locality ad edges of texture image may be igored by the Haar local biary patter texture features owig to strog subjectivity ad poor ability to self-adaptive to the artificial settig judgmet threshold. Therefore, from the perspective of Huma Visio System (HVS), the ew Haar local biary patter texture feature extractio algorithm (HLBP_HVS) is proposed. The local ad global structure iformatio of images are obtaied ad the self-adaptive ad local optimal judgmet threshold are calculated by the aalysis of HVS ifluece factors, which iclude: texture detail ad distributio of spatial positio. The ew Haar local biary patter texture feature HLBP_HVS, which is objective ad coforms to the image texture details ad distributio of spatial positio, could be extracted. The experimetal results show that the proposed algorithm ca effectively avoid the ifluece of the artificial judgmet threshold o the texture detail ad reflects the structure iformatio of the image. Through compariso ad aalysis of the test results, we suggest that the accuracy of classificatio for Brodatz texture library also ca be further improved. Keywords - Texture feature; huma visio system; local biary patter; haar characteristic; HVS ifluece factors I. INTRODUCTION Texture is a visual characteristic which reflects the same pheomeo of images. It is widespread ad difficult to describe ad does ot deped o color or brightess of the image itself. There is some statistical regularity o macroscope for image texture. The extractio of texture feature is the key to classify ad idetify texture image successfully. The commo texture feature extractio methods such as gray level co-occurrece matrix method, wavelet trasform, Tamura texture features ad local biary patter method [1, ]. Local biary patter (LBP) is a operator for image deascriptio that is based o the sigs of differeces of eighborig pixels. Despite beig simple, it is very descriptive, which is attested by the wide variety of differet tasks it has bee successfully applied to [3-6]. Zhou Shure, et al. [7] itroduce the Haar model to the local biary patter method ad combies Gabor wavelet filter to extract the gray image feature i differet directios ad scales. The the ifluece of oise is reduced effectively. Zhou Zhehua, et al. [8] deelope a adaptive LBP by icorporatig the directioal statistical iformatio for rotatio ivariat texture classificatios. Wag Guode, et al. [9] propose a improved CLBP texture feature extractio algorithm. It ca totally describe the texture feature of local widow. It is sesitive to the ueve distributio problem of gray also ca resolve. I Zhou Zhehua, et al. [10], LBP variace is proposed to characterize the local cotrast iformatio ito the oedimesioal LBP histogram. Huma visio has selective ad there is the differet iterest degree i differet part of the image. Therefore, the weight of differet part of the image should be cosidered whe the structure iformatio of image is extracted. The degree of iterest of huma visio is affected by differet factors. Li Guimi, et al. [11] propose five factors which affects the huma visio. The factors are cotrast, size, shape, positio ad color. Other researcher [1, 13] cosider the effect factors of huma visio with three aspects: brightess, texture detail, ad spatial positio. The Haar local biary patter which is combied Haar characteristic with the process of local biary patter is chose to build texture descriptor. It is a biary patter method. The fial feature is a set of biary code. The selectio of threshold T is vital for the Haar local biary patter due to the T decides the biary code which is 1 or 0. The ew Haar local biary patter algorithm is proposed based o the HVS obtais threshold from the image structure iformatio. I view of huma visio ca extract the image structure iformatio ad it is affected by differet factors of image. Therefore, the image structure iformatio that icludes the local ad global iformatio is extracted based o the huma visio effect factors. It is also the basis of selfadaptive threshold T ad HLBP feature. II. HAAR LOCAL BINARY PATTERN OPERATOR A. Local Biary Patter Operator The local biary patter operator [] is a powerful meas of texture descriptio. It is fast to compute ad ivariat to mootoic gray-scale chages of the image. It has prove to be a widely applicable image feature for, e.g., texture classificatio, face aalysis, ad iterest regio descriptio, etc. [14]. The typical versio of the operator labels the image pixels by thresholdig the 3 3-eighborhood of each pixel with the ceter value ad summig the threshold DOI /IJSSST.a ISSN: x olie, prit

2 values weighted by powers of two [3, 4]. The LBP label is obtaied through. 8 p1 p1 LBP ( s( g p g c )) (1) where g c is the gray value of the ceter pixels. g p ( p 1,,...,8) is the gray values of the circularly symmetric eighborhood. s (x) is the thresholdig fuctio. 1, x 0 s ( x) () 0, x 0 B. Haar Local Biary Patter Operator The Haar local biary patter operator [7] is combied Haar characteristic with the process of local biary patter operator. The Haar characteristic is proposed by Viola et al [15]. It is a simple rectagle feature ad ca effectively reflect local variatios of image gray iformatio. The Haar characteristic is show i Fig. 1. It is computed by the gray level differece betwee black ad white rectagles i the image widow. The differece betwee Haar local biary patter operator ad local biary patter operator is as follow: (1) The local biary patter operator threshold the 3 3 eighborhood of each pixel with the ceter value. The traditioal Haar local biary patter operator threshold eight Haar characteristics with the threshold T. The eight Haar characteristics are computed by the gray level values of the image widow which are weighted by eight set of code models that are show i the Fig..The correspodig computatio formula is H k M k W. Where M k is the code model. H k is Haar characteristic.. deotes is poit multiplicatio operatio. () The threshold T of the traditioal Haar local biary patter operator is artificial settig. It decides the fial biary code. 1, x T B( x) (3) 0, x T Figure 1. Haar characteristic. Figure. Eight code models. III. HAAR LOCAL BINARY PATTERN TEXTURE FEA- TURE EXTRACTION ALGORITHM BASED ON HVS The image structure iformatio is extracted ad selfadaptive judgmet threshold T is obtaied by aalyzig huma visio. Some image iformatio affect huma visio. Therefore, the HVS ifluece factors are aalyzed firstly. The, basig it, the chage iformatio of image texture ad spatial positio iformatio is extracted for reflectig the local ad global image structure iformatio. It provides a basis for obtaiig threshold T ad Haar local biary patter feature which are self-adaptive ad satisfied the image structure iformatio. A. HVS ifluece factors Huma visio has certai selectivity. Oly a portio of the regio details of which has a higher resolutio is observed firstly. Because the fudametal characteristic of HVS is sesitive to local cotrast, obvious chage is the regioal iterest of visio ad smooth areas which is uiform brightess or the texture areas which spatial frequecy is close are ofte overlooked. The study of this paper is gray images. Therefore, color chage which is effect o huma visio is igored ad two kids of absolute HVS ifluece factors: texture detail ad spatial positio [1] are discussed. 1) Texture detail factor The sesitivity of differet part of image is differet for HVS. Due to the mai role of huma eye is tracig the outlie of ukow object to perceive the shape of the object, huma eye is sesitive to edge ad stripe structure i the image. By addig the same type ad size of oise accumulatio to the image detail ad flat area, it ca be foud that the degree of visual distortio is differet ad the image detail chages more proouced. Therefore, the paper chooses the gray-value variace to describe the roughess of image texture. The bigger variace represets the richer image texture ad the cocer of huma visio is higher. Coversely, the variace is smaller shows that the area is flat ad spatial frequecy is closig. Therefore, the weight of image icreases with icreasig the variace. Otherwise, the weight of image should be decreased. The texture detail factor is defied as DOI /IJSSST.a ISSN: x olie, prit

3 1 1 ( xk x k ) (4) 1 k1 k1 Where is the umber of image pixels, x k is gray value of the k-th image pixel. ) Spatial positio factor The distributio of light-sesitive cells is deser i the retia's macula, the resolutio of huma eye is highest i the ceter of cetral macula. Peripheral visio that is surrouded by retial rod-shaped cells has low resolutio ad caot see image detail. The cetral part of the image will be oted firstly whe people see a image ad it will be exteded to aroud. It shows that the importace of image is geerally decreasig from cetral to peripheral. So for image widow i, the spatial positio factor is defied as ( x0 xc ) ( y0 yc ) r( 1 (1 Br ) (5) r Where ( x 0, y 0 ) is the cetral coordiate of image widow i. ( x c, y c ) is the cetral coordiate of image. r is the maximum distace betwee the cetral coordiate ad the pixels of image. B r is basic weight. It is related to the size of huma visio ad distace betwee the huma eye ad the image. Geerally, it is set 0~0.5 ad also ca set by the actual circumstaces. it does ot affect the properties of the fial spatial locatio factor. For purpose of calculatio, the paper set B r 0. The spatial locatio factors of image widows which are symmetric with the cetral coordiate of image are same. B. Relative importace of the image pixels Similarly, the relative importace of the image pixels is also geerally decreasig from cetral to peripheral. It coforms to Gaussia distributio. The importace of image pixels ca be defied by the two-dimesioal Gaussia distributio fuctio g ( x, y). For 5 5 image widow, the is defied as, ) 1, ) 0, ) 1, ), ), 1) 1, 1) 1, 1), 1),0) 1,0),1) 1,1),) 1,) 0,) 1,),) 0, 1) 0,0) 0,1) (6) Where g 1,0),0) ( x y ) 1 ( x, y) e. 1,1),1) C. Determiatio of threshold T Due to the extractio object of HLBP feature is image widow, the threshold T is determied by cosiderig the local iformatio ad global iformatio of image. By aalyzig the HVS ifluece factors, the paper obtai the local iformatio texture by extractig the texture detail factor d (. The global iformatio is obtaied by the spatial positio factor r ( which aims at the global image widow is extracted. Therefore, for image widow i, the threshold T i ca be represeted by T i r( (7) For the image widow i, the HLBP _ HVS feature is computed by H k M k W ( k 1,,...,8) (8) 1, x T B( x) (9) 0, x T 8 k1 8k HLBP _ HVS ( B( )) (10) H k Where W is the gray value matrix of the image widow i. D. Descriptio of algorithm Iput: Image to be processed Output: HLBP _ HVS feature Step1: Settig the size of image widow which is the cell of image processed. Accordig to the size of eight code models of HLBP, the size of image widow is set 5 5. Step: Accordig to the code models M k ( k 1,,...,8), the Haar characteristics are computed by the computatioal formula (8). Step3: Obtaiig the local iformatio of image. For the pixels of image widow i, the texture detail d ( is extracted by the computatioal formula (4). Step4: Obtaiig the global iformatio of image. For the pixels of image widow i, the spatial positio r ( ca be extracted by the computatioal formula (5). Step5: Determiig the threshold T i of image widow i. Based o the step3 ad step4, threshold T i of image widow i is computed by the computatioal threshold T i formula (7). The threshold T i is determied by cosiderig the local iformatio ad global iformatio of image. Step6: Extractig the HLBP _ HVS feature set of the image. Based o the threshold T i which is computed by step5, the HLBP _ HVS feature of the image widow i is computed by the computatioal formula (8)-(10). Traversig the etire image by the 5 5 image widow, of which HLBP _ HVS feature set is extracted. IV. EXPERIMENT RESULTS E. Effect degree of ifluece factors o threshold T I order to describe the effect degree of ifluece factors o threshold T. The image is divided ito twety-five equalsized block which is show i Fig. 3. The twety-five blocks are umbered by row ad the colum. The local texture detail factor d (, global spatial positio factor r ( ad DOI /IJSSST.a ISSN: x olie, prit

4 threshold T i are calculated. The effect degree of ifluece factors is show i Fig. 4. samples will be build. The first five sub-samples of each sample are used for traiig ad the after four sub-samples are used for testig based by row ad the colum. The the umber of traiig samples is 555 ad testig samples is 444. Secodly, based o the traiig samples ad testig samples, four methods ( LBP, HLBP, r( ), r( ) ) are used to extract the features sets of traiig samples ad testig samples respectively. The, the feature sets are aalyzed by the histogram method. The statistical results of traiig samples are the fial traiig feature sets. The fial testig feature sets are the statistical results of testig samples. Fially, 1-earest eighbor classifier by the Euclidea distace uder the KNN (k-earest eighbor) classificatio method is used to trai the traiig feature sets which are extracted by four methods ad test ad classify the correspodig testig feature sets. the classificatio accuracy of four methods are calculated ad show i Table I. TABLE I THE CLASSIFICATION ACCURACY OF FOUR METHODS. Figure 3. The image is divided ito twety-five equal-sized block Method Correct Rate(%) LBP HLBP method method T ' = -10 T ' = -5 T ' = 0 T ' = 10 T ' = 0 T ' = 30 T ' = 40 HLBP _ HVS HLBP _ HVS ( r( ) ( r( i )) Figure 4. Effect degree of texture detail, global spatial positio o threshold T i From the Fig. 4, it ca be foud that the spatial positio is geerally ad symmetrically decreasig from cetral to peripheral. The texture detail factors reflect the local iformatio of the image blocks. The spatial positio factors reflect the global iformatio, which is the relatio betwee the image blocks ad the image. The threshold is decided by the texture detail factors ad spatial positio factors. F. Classificatio of Brodatz texture library Brodatz stadard ature texture library is a well-kow baselie database which is used to evaluate the texture recogitio algorithm. Firstly, the sectio divides the each image of Brodatz library (111 images) ito ie subsamples (right ad lower of image remai 1 pixel) ad the size of each sub-sample is 13x13 pixel. The, 999 From the table, the classificatio correct rates of the four methods ca be foud. The correct rate of LBP method is 8.89%. The correct rates of HLBP method are differet because the artificial-settig threshold is differet. The correct rate gradually decreases with icreasig the threshold whe it is betwee 0 ad 40. Furthermore, the correct rate gradually icreases with icreasig the threshold whe it is betwee -10 ad -5 ad the correct rate is equal whe the threshold is -5 ad 0. Therefore, the relatio betwee threshold ad the correct rate is ot liear. The r( ) method calculates the threshold by cosiderig the HVS ifluece factors ad the relative importace of the image pixels. The relative importace ω of the image widow pixels is obtaied by the ideal of Gaussia ad calculated by computatioal formula (6). By the correct rates of the r( ) method ad r( ) method, it ca be foud that the local iformatio should be cosidered moderately. The correct rate of r( ) is higher tha others. It is over 90.3%. Numerical aalysis results idicate, the reasoable threshold is vital ad has a great ifluece o the fial result. the extracted feature set is more accurate ad the correct rate of classificatio for Brodatz is improved ultimately by obtaiig the most objective, selfadaptive ad local optimum threshold. DOI /IJSSST.a ISSN: x olie, prit

5 V. CONCLUSIONS The ew Haar local biary patter texture feature extractio algorithm obtais the reasoable threshold by cosiderig the local iformatio ad global iformatio of image which are represeted by texture detail ad spatial positio of HVS ifluece factors respectively. Experimetal results show, the proposed algorithm successfully solves the artificial-settig threshold problem ad ca validly fuse the local texture chage iformatio ad global positio iformatio, ad also has a better represetatio ability for texture image. REFERENCES [1] Ojala T, Pietikäie M, Harwood D., A comparative study of texture measures with classificatio based o featured distributios. Patter recogitio, vol. 9, o. 4, pp , [] Ojala T, Pietikäie M, Mäepää T., Multiresolutio gray-scale ad rotatio ivariat texture classificatio with local biary patters. Patter Aalysis ad Machie Itelligece, IEEE Trasactios o, vol. 4, pp , 00. [3] Wag X, Ha T X, Ya S., A HOG-LBP huma detector with partial occlusio hadlig. Computer Visio, 009 IEEE 1th Iteratioal Coferece o. IEEE, pp. 3-39, 009. [4] Guo Z, Zhag L, Zhag D., A completed modelig of local biary patter operator for texture classificatio. Image Processig, IEEE Trasactios o, vol. 19, pp , 010. [5] Zhao G, Ahoe T, Matas J, et al., Rotatio-ivariat image ad video descriptio with local biary patter features. Image Processig, IEEE Trasactios o, vol. 1, pp , 01. [6] Liu H, Yag Y Q, Guo X C, et al. Improved LBP used for texture feature extractio. Computer Egieerig ad Applicatios, Vol. 50, pp , 014. [7] Zhou S R, Yi J P., LBP texture feature based o Haar characteristics. Joural of Software, vol. 4, pp , 013. [8] Guo Z, Zhag L, Zhag D, et al., Rotatio ivariat texture classificatio usig adaptive LBP with directioal statistical features. Image Processig (ICIP), th IEEE Iteratioal Coferece o. IEEE, pp , 010. [9] Wag G D, Zhag P L, Re G Q, et al., Texture feature extractio method fused with LBP ad GLCM. Computer Egieerig, vol. 38, o. 5, pp , 01. [10] Guo Z, Zhag L, Zhag D., Rotatio ivariat texture classificatio usig LBP variace (LBPV) with global matchig. Patter Recogitio, vol. 43, pp , 010. [11] Li G M, Ma F Y. Image quality assessmet based o fusio of visual iterest ad HVS characteristics. Sciece Techology ad Egieerig, vol. 7, pp , 014. [1] Yag W, Zhao Y, Xu D. Method of image quality assessmet based o huma visual system ad structural similarity. Joural of Beijig Uiversity of Aeroautics ad Astroautics, vol. 1, o. 5, pp.1-4, 008. [13] Wei G X. Method of image quality evaluatio based o huma visual system ad structural similarity. Joural of Huaihua Uiversity, vol. 11, o. 1, pp. 4-44, 009. [14] Zhag J, Ta T. Brief review of ivariat texture aalysis methods. Patter Recogitio, vol. 35, o. 3, pp , 00. [15] Viola P, Joes M. Rapid object detectio usig a boosted cascade of simple features. Computer Visio ad Patter Recogitio, 001. CVPR 001. Proceedigs of the 001 IEEE Computer Society Coferece o. IEEE, vol.1, o. 8, pp , 001. DOI /IJSSST.a ISSN: x olie, prit