Texture Feature Extraction Inspired by Natural Vision System and HMAX Algorithm

Transcription

1 The Journal of Mathematcs and Computer Scence Avalable onlne at The Journal of Mathematcs and Computer Scence Vol. 4 No.2 (2012) Texture Feature Extracton Inspred by Natural Vson System and HMAX Algorthm Maede Madanan *1, Abbas vafae 2, S.Amrhassan monadjem 3 Receved: January 2012, Revsed: Aprl 2012 Onlne Publcaton: June ,2,3Department of Computer Engneerng, Faculty of Engneerng, Unversty of Isfahan, Isfahan, 81746, Iran {m_madanan, abbas_vafae, monadjem}@eng.u.ac.r ABSTRACT In ths paper, a new and effectve method called HMAX s used for mage texture. feature extracton. Ths method s nspred by the bologcal system of bran and human vson n order to create feature vectors for mage recognton. A set of C2 features obtaned from HMAX algorthm that are stable aganst changes n angle and sze, are extracted from all mage datasets frstly. Then usng artfcal neural networks and K- nearest neghbor classfers, eght dfferent types of natural texture mages from VISTEX dataset are classfed. In order to evaluate the HMAX feature extracton method, the classfcaton results are compared wth Gabor flter banks. Snce HMAX model s consstent wth natural vson system, t s expected to obtan a better accuracy compared to Gabor flter banks. Expermental results wth artfcal neural network and K-nearest neghbor classfer show that the accuracy of 90.12% and 84.50% respectvely for HMAX features. They have sgnfcant mprovements compared to Gabor flter banks whch obtaned 78.62% and 72% accuracy. Keywords: texture feature extracton, HMAX model, texture classfcaton, artfcal neural network, K-nearest neghbor 1.INTRODUCTION * Correspondng Author 197

2 In recent years, great attenton has been pad to the texture features and ther extracton n the feld of mage processng and machne vson and Feature vectors extracton from mages usng texture, can be the base of many other processes such as classfcaton, segmentaton and dentfcaton of objects. Texture classfcaton tself s an mportant ssue n the feld of mage analyss as well. Medcal X-erography, surface nspecton, and documentaton are only a few examples of applcatons n whch texture classfcaton plays a key role. Usually n the texture classfcaton, frstly the features have been extracted and then the features would be classfed.typcal ways to extract the features of mage texture can be classfed nto four categores of:[1] 1.Model-based methods: Ths scheme s bascally establshed on the structure of a vsual model that not only descrbes the texture but also syntheszes t. Models are capable of smulatng the regonal texture nformaton by means of mult-varable and dependent probablty dstrbuton functons[2]. 2.Syntactc or structural methods: In ths approach, texture, as an adjusted array, conssts of a number of constant categorzed pxels called the prmate components. These models suppose that the prmate components are located n a place wth a rather adjusted connecton and texture can be descrbed by these components together wth a set of alternaton and connecton rules. Then, through adjustng the textures wth the gven rules and components, mages can be parttoned and classfed. 3.Statstcal methods: These methods are performed on the bass of locatonal (spatal) dstrbuton of gray levels n a texture, ncludng several categores tself: - Frst-order statstcal methods usng gray surfaces hstograms n a texture. - Second-order statstcal methods, such as Co-occurrence matrces, whch s one of the prmary methods n extractng texture features and ndcates the second order statstcal propertes of an mage[3]. - Auto correlaton functon s descrbed by means of auto correlaton coeffcent ndcatng the lnear locatonal correlaton among the pxels. - Spectrum of a texture unt, whch frstly alternates texture pxels wth texture unts (functons of rather small neghbors around a pxel), and then, measures texture unts dstrbuton (so-called texture spectrum) on the magograph. Ths method, n fact, descrbes the locatonal relaton among mage pxels n a gven neghborhood. 4. Sgnal processng methods: As for these methods, texture s modeled as a twodmensonal dgtal sgnal. These methods have always been attractve and capable of extractng features properly, on both natural and Artfcal textures. One of these methods, s the Mult-Scale,Mult-Drectonal methods (MSMD). The texture-analyss MSMD methods through emulatng human vsual systems partton the nput mage by some flters nto partal mages wth varant frequences n dfferent orentatons. Each of these partal mages ncludes part of the nput mage features. Among MSMD methods are Gabor flters and wavelet transform[4],[5]. As for the popularty of these extractng features and ther correspondence wth natural vsual content, the method utlzed n ths artcle, whch s also nspred from object recognton structure n natural vsual layers, called HMAX, can be categorzed as one of these methods. The common tasks performed by Poggo and Marr caused the exstence of HMAX model fnally by Resenhuber and Poggo, amed at emulatng the human vsual system behavor n ventral channel [6]. Smlar to Marr theory, n the ntal stages of vsual system, the cells, respond to smple and prmtve forms lke gratngs, bars, edges, 198

3 etc [7]. Yang models these cells behavors through Gaussan functons [8]. In HMAX model, S1 layer emulates the fst-stage vsual system by means of Gabor flterng. Followng the studes conducted by Poggo team, n 2007, a new model was ntroduced by Thomas Sr to emulate the recognton mechansm n bran [9], [10]. The man dfference between ths model and the orgnal HMAX model s the addton of a learnng step. The applcatons of ths model for machne vson ssues, ncludng object dentfcaton and recognton, have proved to be hghly effcent [9]. Ths model was examned by Mrz and Wolf n face recognton [11]. As proposed by Mrz, after performng certan preprocessngs on the mage, the output C1 layer n new model s measured and then through usng RCA analyss, features called S2FF are produced to descrbe the face. Beng tolerant of angle and sze changes, these features outperform all other methods. Snce the HAMX model s consstent wth natural vsual systems, we expect ts effcency and accuracy n dagnoss of objects and texture classfcaton. In ths paper, we studed the performance of ths algorthm n extracton of the features of natural texture mages wth random textures, and then compare ts performance wth the Gabor flter bank algorthm. For mages' classfcaton, we used the K-Nearest Neghbors (KNN) and Artfcal Neural Networks (ANN) classfcaton and fnally we compared ther performances too. 2. Image feature-extractng HMAX method In ths secton, the object recognton structure of human and anmals vsual system s frstly ntroduced and analyzed. Then, the qualty of extractng mage texture features by the proposed HMAX model, to classfy the mage, s descrbed Object recognton structure Snce human and anmals vsual systems operate well, t has been long desred to buld a model smulatng that. Durng the frst mllseconds of human and some anmals vsual operaton, a herarchy system n bran s used for object recognton. It s beleved that nformaton n vsual layer s flown nto two Ventral and Dorsal pathways. Object recognton n vsual layer s performed through Ventral pathway. Fgure 1 llustrates a monkey ventrally orented vsual pathway. As shown, receved nformaton from retna, after beng transferred to LGN unt, s transferred to V1 layer, V4 layer, and fnally the IT. Eventually, IT contrbutes to vson adjustment and object sghtng, and therefore, the ntended object s recognzed [12]. Fgure1. The ventral vsual stream of the monkey 199

4 In recent decade, Poggo and hs colleagues tred to perceve and quanttavely duplcate the structure and ventral-orented vsual processngs of human neural system durng the object recognton process. These attempts lead to the ntroducton of a new method called HMAX. In general, HMAX model, conssts of 4 layers ncludng alternatve smple S and complex C layers [13]. C1 and S1 layers duplcate (model) smple and complex neurons behavor n V1 area of human s vson. C2 and S2 layers behave smlarly to neurons n areas above the human V1 vsual area. S layers compound out nputs usng a Gaussan operaton, whch results n respondng to more specfc objects. C layers, also, are more complex unts compoundng S layer output as the nput wth maxmum operaton. Ths regularly creates tolerance to sze and scale changes. Ths model can quanttatvely duplcate the general propertes of neurons n nferotemporal monkey cortex [14].Fgure 2 outlnes the HMAX model. Fguer2. HMAX Model Frst, the nput mages n smple unts (S1), wth the contrbuton of 2- dmensonal flters arrayed n 4 varant orentatons, are sampled concsely. Wthn compound band area, S1 cells nformaton, a group of cells n the same orentaton but rather varant scales and postons, are transferred to complex cells (C1) through MAX operaton. Then, n S2 level, and for each frequency band a group of S1 flters wth smlar frequences lmt, a square-shaped area consstng of four C1 unts are entered nto a sngle S2 unt. As a result, all flters responses become tolerant to scale changes. The entre nput mage s, also, nvarant to poston changes. Fnally, the S2 unts are re-compound through the 200

5 maxmum operaton to pass the necessary nput to C2 unts. When the C2 stage beng passed, the extracted mage features are obtaned C2-features extracton In ths stage, through the utlzaton of HMAX method, the C2 features of texture mages used n ths artcle are extracted. As stated n the prevous secton, the algorthm to extract the above-mentoned features conssts of four layers of alternatve S1, C1, S2, and C2. C2 through the HMAX algorthm s as follows: [9] S1 layer: The nput mage wth gray surfaces levels enters as a one-dmensonal array nto S1 layer. In S1 layer, Gabor flters wth varant orentatons (θ) and wdths (σ) appled to the nput mage. Equaton 1, shows how ths flter s appled to the nput mage: [9] In ths relaton, X=xcosө+ysnө and Y=-xsnө+ysnө. (1) In vsual cortcal V1 area, there are some neurons, each beng senstve to a partcular edge, n terms of sze and angle. Thus, n S1 layer, Gabor flter s used to smulate V1 neurons and each flter s appled to each pxel of nput mage. Gabor flter s appled to the nput mage n 4 orentatons (θ) and 6 scales (S). Therefore, 16 4 maps are obtaned that are arranged n 8 bands. For each two scales, one band s used, and totally 8 bands are appled. For nstance, accordng to the adjusted parameters n Table 1, band 1 conssts of flter output n two scales (szes 7, 9) wth all orentatons and band 2, conssts of flter output n two scales (sze 11,13). Followng that, Gabor flter s appled to nput mages. Gabor flter adjustments on S1 layer s llustrated n Table 1[9]. Accordng to Table 1, the permssble scale lmts and poolng through Gabor flters are determned n relatons 2 and 3: Scale range= { } (2) pool range ={ } (3) Tabel1. Adjusted parameters used n our mplementaton [9] Band Flt.sze 7&9 11&13 15&17 19&21 23&25 27&29 31&33 35&37 σ 2.8& & & & & & & &18.2 Λ 3.5& & & & & & & &22.8 Grd Sze Orent 3 0, 4, 2, 4 Patch Sze

6 C1 layer: Ths layer conssts of complex neurons tendng to cover larger receptve felds. Accordngly, ths layer responds to all edges and orentatons anywhere wthn ther receptve felds. Ths layer s tolerant to scale and mage movement shfts. To obtan C1 through the obtaned data of S1 layer, a good number of algorthms are offered. Hubel and Wesel proposed a structure and method to extract C1 from S1[14]. Poggo and Huber also presented another method for layers structures leadng to C1 extracton from S1[15]. These methods mostly appled max operator to obtan the requred data n C1 layer, consderng the obtaned data from S1 layer. For each band (1 to 8) the maxmum of scales and postons are obtaned. To be more precse, n each 4 orentatons of two avalable band scales, a grd cell of sze N Σ N Σ s used and then, a maxmum of two scales n each orentaton s taken. For nstance, (as shown n Table 1) n band 1, one grd cell of sze 8 8 s appled on each orentaton of scales 7 and 9. Fnally, for each partcular orentaton, a max s taken. It must be noted that the maxmum s exactly taken between two equal orentatons and ths s repeated for each of the four orentatons. Thus, n C1, each band conssts of four maps C 1 maps. S2 layer: A large pool of patches of varous szes at random postons are extracted from a target set of mages at the C1 level and for all orentatons,.e. a patch P of sze n n contans n n 4 elements, where the 4 factor corresponds to the four possble S1 and C1 orentatons. In ths smulaton, patches of sze n =4, 8, 12, 16 are used. But, n practce, any sze can be consdered. The tranng process ends by settng each of those patches as prototypes or centers of the S2 unts whch behave as radal bass functon (RBF) unts durng recognton. In other word, each S2 unt response depends n a Gaussan-lke way on the Eucldean dstance between an nput patch (at a partcular locaton and scale) and the stored prototype. Ths phenomenon s consstent wth wellknown neuron response propertes n prmate nferotemporal cortex and seems to be the key property for learnng to generalze n the vsual systems [16]. When a new nput s presented, each stored S2 unt s convolved wth the new C1 nput mage at all scales, Ths leads to K 8 S2 mages, where the K corresponds to the patches extracted durng learnng and the 8 factor, to the 8 scale bands. As for the conducted experments n ths feld, the more the number of extracted patches are, the hgher the classfcaton accuracy wll be. C2 layer: after takng a fnal max for each S2 feature vectors, the K fnal C2 features are obtaned. These features are ndependent of movements and scale changes. 3. Results and experments In ths secton, frstly, the data set appled n ths artcle s ntroduced. Then, the experments procedure and the results obtaned from applyng the proposed methods are surveyed. 202

7 3.1. Data set In the present artcle, the VISTEX standard texture data set s used n order to nvestgate the proposed-methods operaton [17]. Fgure 3 llustrates eght texture types of natural texture mages used for classfcaton. The twn mages shown of each class are samples of tranng and test stage, respectvely. WATER FABRIC2 WHER-WALDO BRICK FOOD FOOD2 BARK TERRAIN Fgure 3: Samples of group of texture mages appled n classfcaton process 3. 2.Natural texture mages classfcaton After extractng texture features through the proposed HMAX model, mage classfcaton wll be done. Ths phase conssts of 2 stages. Frst, classfers are traned through extracted features of tranng mages. Then, classfcaton accuracy s tested and determned through unknown mages whch are not tolerant to rotaton and scale shfts compared to tranng mages. In ths artcle, two classfers of feedforward neural network and the K-nearest neghbourhood are appled, and fnally, the results obtaned thereof are compared. Accordng to the expermental fndngs, utlzaton of the feedforward neural network classfer ncreases the system accuracy, compared to the K-nearest neghbourhood. Fgure 4 s offered as an llustraton. 203

8 ANN KNN Fgure 4. Comparng classfcaton performance of 8-type textures, based on HMAX feature extracton algorthm usng K-nearest neghbourhood and feedforward neural network In order to nvestgate the proposed algorthm accuracy, the result of classfcaton obtaned from ths algorthm was compared to the Gabor flter bank. The results show that HMAX method s much more precse and effcent. Fgure 5 s a representatve of such supremacy HMAX Gabor Fgure 5. Comparng classfcaton performance of 8-type texture usng HMAX featureextractng algorthm and Gabor, and classfer of feedforward neural network In Table 2, the general performance obtaned from classfyng 8-type natural textures s offered. As expected, the general effcency and accuracy of the proposed method s desrable n classfyng random natural textures. As that table shows, HMAX on neural network classfer s 11.5% more accurate than Gabor, whle agan outperforms t on KNN wth 12.5% hgher accuracy 204

9 4. Concluson Table2.overall classfcaton accuracy usng 8 texture classes Feature extracton method HMAX Gabor HMAX Gabor classfer ANN ANN KNN KNN accuracy )%( In ths paper, the effectve and approprate method, named HMAX, s used to extract texture features. The structure of ths method has been nspred form the vsual cortex of the anmal bran functon n detectng objects. Wth the mplementaton of ths method on VISTEX data set, we acheved the consderable spatal accuracy compared to the performance obtaned from the known Gabor flter bank algorthm. After a promosng feature extracton from natural texture mages, two classfers of Artfcal Neural networks and K-Nearest neghbor are used to classfy the features extracted from textures' mages. By comparson of the results we found that the ANN classfer performance s hgher than the K-Nearest Neghbor. Regardng ths fact that the proposed classfcaton method s assocated wth hgh accuracy and effcency n the context of natural texture mages, we ntend to mplement texture segmentaton system based on that successful accomplshed classfcaton, as a future work. REFERENCE [1] N. Ban, Evaluaton of Texture Features For Analyss of Ovaran Follcular Development, MS Thess, Department of Computer Scence Unversty Saskatchewan, Saskatchewan, Saskatoon,2006. [2] S. Krshnamachar and R. Chellappa, Multresoluton Gauss Markov Random Feld Models for Texture Segmentaton, IEEE Transactons on Image Processng, Vol. 6, (6),pp , [3] ] G. Cooper, The textural analyss of gravty data usng co-occurrence matrces, Elsever Journal of Computers & geoscences, Vol. 30.(1),pp , 2004 [4] N. Sgnolle, M. Revenu, B. Plancoulane and P. Herln, Wavelet-based multscale texture segmentaton: Applcaton to stromal compartment characterzaton on vrtual sldes, Elsever Journal of Sgnal Processng,Vol. 90,(8), pp , [5] M. L and R. Staunton, Optmum Gabor flter desgn and local bnary patterns for texture segmentaton, Elsever Journal of Pattern Recognton Letters,Vol. 29, (5), pp , [6] M. Resenhuber and T. Poggo, Herarchcal models of object recognton n cortex, Nat. Neurosc, Vol. 2, (11), pp , [7] D. Marr, Vson: A Computatonal Investgaton nto the Human Representaton and Processng of Vsual Informaton. San Francsco: WH Freeman and Co,

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