Extraction of Texture Information from Fuzzy Run Length Matrix

Transcription

1 Internatonal Journal of Computer Applcatons ( ) Volume 55 o.1, October 01 Extracton of Texture Informaton from Fuzzy Run Length Matrx Y. Venkateswarlu Head Dept. of CSE&IT Chatanya Insttuteof Engg. &Tech.,Rajahmundry, Inda. B. Sujatha Assoc. prof., Dept. of Computer Scence & Engg. Godavar Insttute of Engg.& Tech.,Rajahmundry, Inda. V. Vjaya Kumar, PhD. Dean Dept.of Computers, Godavar Insttute of Engg.& Tech., Rajahmundry, Inda. ABSTRACT For a precse texture classfcaton and analyss, a run length matrx s constructed on the Local Bnary pattern usng fuzzy prncples n the present paper. The proposed Run Length Matrx on Fuzzy LBP (RLM-FLBP) overcomes the dsadvantages of the prevous run length methods of texture classfcaton that exst n the lterature. LBP s a wdely used tool for texture classfcaton based on local features. The LBP does not provde greater amount of dscrmnate nformaton of the local structure and t has a varous other dsadvantages. The man dsadvantage of LBP s, that t compares the centre pxel value wth ts neghbors to derve the one of the three possble values {0, 1, }. The basc drawback of ths comparson s that t s very senstve to nose. And a major contrast between the central pxel and ts surroundngs are easly resulted by the slght fluctuatons above or below the value of the Centre Pxel (CP) and ts surroundngs. To overcome ths problem and to represent the mssng local nformaton effectvely n the LBP, the present study ntroduced the concept of fuzzy logc on LBP. Ths overcomes the problem related to nose and contrast. The proposed method ntally converts the 3 3 neghborhood n to fuzzy LBP. In the second stage the proposed method constructs the Run Length Matrx on Fuzzy LBP (RLM- FLBP). On these RLM-FLBP texture features are evaluated for a precse texture classfcaton. Keywords: Run Length Matrx, Fuzzy LBP, Centre Pxel, Local Structure.. 1. ITRODUCTIO Galloway proposed the use of run length matrx for texture feature extracton [1]. The run length matrx proposed by Galloway has not been wdely used as an effectve texture classfcaton and analyss method, because these run length features are proved to be the least effcent texture features among a group of tradtonal texture features such as cooccurrence features, the grey level dfference features etc. To overcome ths, the present thess nvestgated a new approach that s dervaton of run lengths on FLBP. The Local Bnary Pattern (LBP) approach has evolved to represent a sgnfcant breakthrough n texture analyss, outperformng earler methods n many applcatons. Perhaps the most mportant property of the LBP operator n real-world applcatons s ts tolerance aganst llumnaton changes. Another equally mportant s ts computatonal smplcty, whch makes t possble to analyze mages n challengng realtme settngs. Image texture analyss s an mportant fundamental problem n computer vson. Durng the past few years, several authors have developed theoretcally and computatonally smple, but very effcent nonparametrc methodology for texture analyss based on LBP [, 3, 4, 5, 6, 7, 8, 9]. The LBP texture analyss operator s defned as a grayscale nvarant texture measure, derved from a general defnton of texture n a local neghborhood. For each pxel n an mage, a bnary code s produced by thresholdng ts value wth the value of the center pxel. A hstogram s created to collect up the occurrences of dfferent bnary patterns. The basc verson of the LBP operator consders only the eght neghbors of a pxel, but the defnton has been extended to nclude all crcular neghborhoods wth any number of pxels. [10, 11, 1] Through ts extensons, the LBP operator has been made nto a really powerful measure of mage texture, showng excellent results n terms of accuracy and computatonal complexty n many emprcal studes. The LBP operator can be seen as a unfyng approach to the tradtonally dvergent statstcal and structural models of texture analyss. Perhaps the most mportant property of the LBP operator n real world applcatons s ts tolerance aganst llumnaton changes. Another equally mportant s ts computatonal smplcty, whch makes t possble to analyze mages n challengng realtme settngs. That s why the LBP method has already been used n a large number of applcatons all over the world, ncludng vsual nspecton, mage retreval, remote sensng, bomedcal mage analyss, face mage analyss, moton analyss, envronment modelng, and outdoor scene analyss. The present study developed run length matrx on fuzzy LBP. The present paper s organzed as follows. The secton two descrbes Representaton of LBP. Secton 3 descrbes dervaton of the proposed RLM-FLBP and the computaton of texture features, secton four s for expermental analyss and conclusons are descrbed n secton fve.. Representaton of LBP The present secton ntroduces the basc concept of LBP. It s a gray-scale nvarant texture measure computed from the analyss of a 3 3 local neghborhood over a central pxel. The LBP s based on a bnary code descrbng the local texture pattern. Ths code s bult by thresholdng a local neghborhood by the gray value of ts center. In a square-raster dgtal mage, each pxel s surrounded by eght neghborng pxels. The local texture nformaton for a pxel can be extracted from a neghborhood of 3 3 pxels, whch represents the smallest complete unt (n the sense of havng eght drectons surroundng the pxel). A neghborhood of 3 3 pxels s denoted by a set contanng nne elements: P= {P 0, P 1...P 8 }, here P 0 represents the ntensty value of the central pxel and P {=1, 8}, s the ntensty value of the neghborng pxel. The eght neghbors are labeled usng a bnary code {0, 1} obtaned by comparng ther values to the central pxel value. If the tested gray value s below the gray value of the central pxel, then t s labeled 36

2 Internatonal Journal of Computer Applcatons ( ) Volume 55 o.1, October 01 0, otherwse t s assgned the value 1 as descrbed by the Equaton (1). { and (1) d s the obtaned bnary code, P s the orgnal pxel value at poston and P 0 s the central pxel value. The Fg.1(a) shows the grey level values of a 3 3 neghborhood of an mage. And the Fg.1(b) shows ts correspondng bnary labelng based on Equaton (1). The bnary weghts of the gven 3 3 neghborhood are calculated by the Equaton (). As each element of LBP has one of the two possble values, the combnaton of all the eght elements results n 8 = 56 possble local bnary patterns rangng from 0 to 55. There s no unque way to label and order the 55 LBP on a 3 3 neghborhood (a) (b) (c) Fg.1 (a) Sample Grey level eghborhood (b) Converson of Fg.1 (a) nto Bnary eghborhood (c) Representaton of Fg.1 (a) Bnary Weghts (d) Represented Values wth Bnary Weghts. Fg.1 shows an example on how to compute LBP. The orgnal 3 3 neghborhood s gven n Fg.1(a). The central pxel value s used as a threshold n order to assgn a bnary value to ts neghbors. Fg.1(b) shows the result of thresholdng the 3 3 neghborhood. The obtaned values are multpled by ther correspondng weghts as shown by Fg.1 (c). The result s gven n Fg.1(d). The sum of the resultng values gves the LBP measure whch s 7 n ths case the central pxel 40 s replaced by the obtaned LBP value 7. A new LBP mage s constructed by processng each pxel and ts 3 3 neghbors n the orgnal mage. The bnary weghts of Fg.1(c) can be gven n eght dfferent ways. 3. The Proposed Method of Run Length Matrx on Fuzzy Local Bnary Pattern (RLM-FLBP) The major problem of the above approach of LBP s t fals n dealng accurately wth the regons of natural mages n the presence of nose, contrast, llumnaton changes and the dfferent processes of capton and dgtzaton. For example, even f the human eye perceves two neghborng pxels as equal, they rarely have exactly the same ntensty values. However, the desrable stuaton would be that the LBP of homogeneous mages contan more number of ones because the human eye can perceve ones. That s LBP takes a value 1 for any dfference (mn to max) of values. Therefore, f there s lack of ones, the basc LBP wll take only 0 value, whch means that the real number of possble textures are 8,.e., 56. To overcome the above, the fuzzy membershp (d) functon s ntroduced by the present study on LBP. To have more vsual clarty on dfference of values between central pxel and neghborng pxel fuzzy logc s establshed, whch gve a set of values between 0 to 1 as {0, 0.1, 0., 0.3, 0.4,..., 1} on the LBP neghborhood. Fuzzy logc has certan major advantages over tradtonal Boolean logc when t comes to real world applcatons such as texture representaton of real mages. The man dfference between the fuzzy and the classc logc s that statements are no longer 0 or 1 n fuzzy, but assume any real value between 0 and 1, that allows more human-lke nterpretaton and reasonng. The ncorporaton of fuzzy logc by the present study nto the LBP approach ncludes the transformaton of the nput varables to respectve fuzzy varables, accordng to a set of fuzzy rules. Based on ths assumpton the present paper derved fuzzy rules on 3 3 LBP neghborhood to descrbe the relaton between the ntensty values of the neghborng pxels P and the center pxel P 0 n a more human percepton vew pont. The fuzzy rules of the present approach on LBP are gven below. Rule 0: The more negatve s, the greater the certanty that d s 0. Rule 1: The more postve s, the greater the certanty that d s 1. These two rules are rewrtten n terms of two membershp functons are defned n Equatons (3) and (4) as follows: { (3) { (4) where s a decreasng functon, s an ncreasng functon, G [0,55] represents a parameter that controls the degree of fuzzness. Fnally, for a commtment between relablty and accuracy, the membershp functons provde the membershp degrees to whch a pxel s lghter or darker than the central pxel of a 3 3 LBP raster wndow. Usng the above fuzzy functonal rules, the Fuzzy Local bnary Pattern (FLBP) of the neghborng pxels s gven by Equaton (5). {( ) ( ) ( )} The Equaton(4) can be rewrtten as n Equaton(6) (( ) ) (( ) ) { (( ) ) By the above equatons the FLBP converts a 3 3 wndow neghborng pxel values nto the FLBP set,.e., FLBP {0, 0.1, 0., 0.3, 0.4,...,1}. The average membershp values of FLBP neghborng pxels are useful for characterzaton of textures. But sometmes t s dffcult to evaluate. To address ths dffculty the present approach derved Run length matrx (RLM) on the FLBP of the mage. By RLM-FLBP a set of ponts are obtaned and each set has ts own run length entropy dmenson as descrbed below. For a gven mage, the proposed method defnes a RLM(,j) on FLBP as number of runs startng from locaton } (5) 37

3 Internatonal Journal of Computer Applcatons ( ) Volume 55 o.1, October 01 (,j) of the FLBP mage. Ths may produce an n number of RLM-FLBP, whch may become a complex procedure for texture analyss. To address ths, the present paper concsed the number of RLM-FLBP based on some lag value as descrbed below. The proposed method derved fve dfferent RLM-FLBP s. The RLM-FLBP 1 contans the run length values for zero and RLM-FLBP contans the run length values from 0.1 and 0.3, RLM-FLBP 3 contans the run length values from 0.4 and 0.6, RLM-FLBP 4 contans the run length values from 0.7 and 0.9, RLM-FLBP 5 contans the run length values of 1 respectvely. The Fg. and Fg.3 explans the proposed method of generatng fve RLM-FLBP mages s as follows: Fg.: Fuzzy Local Bnary Pattern (FLBP) Image. RLM-FLBP 1 RLM-FLBP RLM-FLBP 3 RLM-FLBP RLM-FLBP Fg.3: Fve dfferent RLM-FLBP s on FLBP mage of Fg.. The proposed RLM-FLBP derved fve fuzzy run-length matrces such as RLM-FRLM 1, RLM-FRLM, RLM-FRLM 3, RLM-FRLM 4, RLM-FRLM 5, whch are unque varatons of the fuzzy run-length matrx. Fnally, fve fuzzy run-length matrces are combned to form a sngle matrx called as RLM- FLBP. 3.1 Computaton of Texture Features Two sets of texture features are derved from RLM- FLBP for texture classfcaton. The frst set of features used from the FRLM (Weska et al., 1976 [13]) s average energy (e 1 ), energy (e ), entropy (e 3 ) and standard devaton (e 4 )) as n Equatons (7)-(10) and the second set of features obtaned contans hgher order statstcs [13] nclude Small number Emphass ( 1 ), Large number Emphass ( ), on Unformty ( 3 ) and Second Moment ( 4 ) as gven n Equatons (11)-(14). These features are stored n the features lbrary. M L orm-1/ Average Energy (e 1 )= 1 (7) FRLM M L k1 s1 M L 1 orm-/energy (e ) = FRLM (8) L M k1 s1 1 M L Entropy(e 3 ) = FRLM log ) (9) M L k1 s1 Standard Devaton (e 4 )= M L 1 (10) ( FRLM Mean ) M L Small number Emphass Large number Emphass on Unformty Second Moment 8 k1 s1 9 k 1 s1 1 k 1 s1 k 1 s1 8 ( FLRM / s k 1 s1 k1 3 8 FLRM ( FLRM s ) 9 s1 9 k1 s1 k 1 s1 4 k 1 s1 ) FLRM FLRM (11) (1) FLRM (13) ( FLRM ) FLRM (14) 4. Expermental Results Experments are carred out to demonstrate the effectveness of proposed RLM-FLBP method for texture mage classfcaton. The proposed method s expermented wth OuTex [14] and Grante [15] color mage databases, as gven n Fg.1.1 and 1. respectvely. To do classfcaton, the texture mages are frst dvded nto non-overlappng wndows of sze 3 3 and the resultng wndows are then dvded nto two dsjont sets, one for tranng and one for testng. Two dstance classfers (Manhattan dstance (d 1 ) and Eucldean dstance (d )) are used to choose the best classfcaton technque wth the proposed RLM-FLBP. The classfer computes the dstance between the features for each sample and that of the texture classes and assgns the unknown sample to the texture class wth the shortest dstance. The classfcaton results for each of the two feature sets are shown n Table I, Table II, Table III and Table IV respectvely. The frst observaton s that the performance of the two tests s affected very much by dfferent choces of energy measures. For example, standard devaton s more sutable for the proposed RLM-FLBP features than any other norm. Thus, when testng textures extracted from the OuTex album, the best performance s acheved by havng the statstcal measure (e 4 ) and the dstance measure (d ). The second observaton s based on hgher order statstcal features. It s observed that the features 1 and are also resultng n good classfcaton rate. Therefore the present paper consders e 4 from feature set-1 and 1, from feature set- for classfyng textures. Smlarly, by observng the features of second dataset (Grante album) e 4 perform hgh accuracy than the other energy features. And the features 1 and are also resultng n good classfcaton rate. By the above observatons the proposed study concludes that the features of 38

4 Internatonal Journal of Computer Applcatons ( ) Volume 55 o.1, October 01 e 4, 1 and form a good feature set. Table V summarze the overall classfcaton accuracy usng two datasets of two classfers and also two feature sets. Table 1: Results of texture classfcaton usng energy features of OuTex Database. Dstance Energy OuTex: % of mean classfcaton rate FRLM 1 FRLM FRLM 3 FRLM 4 FRLM 5 Average d 1 e e e e d e e e e Table : Results of texture classfcaton usng hgher order statstcal features of OuTex Database. Hgher order Dstance OuTex: % of mean classfcaton rate statstcal features FRLM 1 FRLM FRLM 3 FRLM 4 FRLM 5 Average d d Table 3: Results of texture classfcaton usng energy features of Grante Database. Dstance Energy Grante: % of mean classfcaton rate FRLM 1 FRLM FRLM 3 FRLM 4 FRLM 5 Average d 1 e e e e d e e e e Table 4: Results of texture classfcaton usng hgher order statstcal features of Grante Database. Grante: % of mean classfcaton rate Dstance Hgher order statstcal features FRLM 1 FRLM FRLM 3 FRLM 4 FRLM 5 Average d d

5 Internatonal Journal of Computer Applcatons ( ) Volume 55 o.1, October 01 Table 5: Mean classfcaton rate of selected features of two datasets. Dstance d Energy OuTex: % of mean classfcaton rate e Average Grante: % of mean classfcaton rate 4.1 Comparson of the Proposed RLM- FLBP wth other Methods Table 5 shows the mean percentage classfcaton rate for two datasets of texture mages by usng the proposed RLM- FLBP. Other exstng methods of gray level run length matrx are by Xaoou Tang [16] and bnary run length matrx by Ramana Reddy etal [17]. These methods are appled on VsTex, Marble texture databases and are represented n Table 6. From Table 6, t s clearly evdent that, the proposed RLM- FLBP exhbts a hgh classfcaton rate than the exstng methods. The graphcal analyss of the percentage mean classfcaton rate for the proposed RLM-FLBP and other exstng methods are shown n Fg.4. Table 6: Comparson of the proposed RLM-FLBP method wth other exstng methods Database/ Method VsTex Marble Tradtonal Run Length by Xaoou Tang [16] Bnary Run Length by Ramana Reddy etal[17] Proposed Method: Fuzzy Run Length COCLUSIOS The proposed RLM-FLBP overcomes the dsadvantages of the prevous Run length matrces for texture classfcaton. In the proposed approach Run lengths are evaluated on the fuzzy LBP. LBP s an effcent tool n the proposed approach overcomes the tradtonal problems of LBP on nose, contrast and llumnaton changes. The proposed approach reduced the number of dfferent Run length matrces by consderng the lag value on the FLBP mage. The proposed RLM-FLBP shows a better performance when compared to exstng methods. 6. ACKOWLEDGMETS The authors would lke to express ther grattude to Sr K.V.V. Satya arayana Raju, Charman, and K. Sash Kran Varma, Managng Drector, Chatanya group of Insttutons for provdng necessary nfrastructure. Authors would lke to thank anonymous revewers for ther valuable comments and Dr. G.V.S. Ananta Lakshm for her nvaluable suggestons whch led to mprovse the presentaton qualty of ths paper 7. REFERECES [1] M. M. Galloway, Texture analyss usng gray level run lengths, Comput. Graphcs Image Process., vol. 4, pp , June [] Ahonen T., Hadd A. and Petkanen M., Face Recognton wth Local Bnary Patterns, Computer Vson, ECCV Proceedngs, pp , 004. [3] Ahonen T., Petkanen M., Hadd A. and Maenpaa T., Face Recognton Based on the Appearance of Local Regons, 17th Internatonal Conference on Pattern Recognton III: pp , 004. [4] Feng X., Hadd A. and Petkanen M., A Coarse-to- Fne Classfcaton Scheme for Facal Expresson Recognton, Image Analyss and Recognton, ICIAR 004 Proceedngs, Lecture otes n Computer Scence 31 II: pp , 004. [5] Feng X., Petkanen M. and Hadd A., Facal Expresson Recognton wth Local Bnary Patterns and Lnear Programmng, Pattern Recognton and Image Analyss 15 pp , 005. [6] Hadd A., Petkanen M. and Ahonen T., A Dscrmnatve Feature Space for Detectng and Recognzng Faces, IEEE Conference on Computer Vson and Pattern Recognton II: pp , 004. [7] Hekkla M., Petkanen M. and Hekkla J., A Texture- Based Method for Detectng Movng Objects, The 15th Brtsh Machne Vson Conference I: pp , 004. [8] Takala V., Ahonen T. and Petkanen M., Block-Based Methods for Image Retreval Usng Local Bnary Patterns, Image Analyss, SCIA 005 Proceedngs, Lecture otes n Computer Scence, 005. [9] Turtnen M. and Petkanen M., Vsual Tranng and Classfcaton of Textured Scene Images, 3rd Internatonal Workshop on Texture Analyss and Synthess pp , 003. [10] Maenpaa T. and Petkanen, M., Texture Analyss wth Local Bnary Patterns, Handbook of Pattern Recognton and Computer Vson, 3rd edn. World Scentfc pp , 005. [11] Ojala T., Petkanen M., Harwood D., A Comparatve Study of Texture wth Classfcaton Based on Feature Dstrbutons. Pattern Recognton, pp , [1] Ojala T., Petkanen M., Maenpaa T., Multresoluton Gray-Scale and Rotaton Invarant Texture Classfcaton wth Local Bnary Patterns, IEEE Transactons on Pattern Analyss and Machne Intellgence 4 pp , 00. [13] J. S. Weszka, C. R. Dyer, and A. Rosenfeld, A comparatve study of texture measures for terran classfcaton, IEEE Trans. Syst., Man, Cybern., vol. SMC-6, pp , [14] Ojala T., Petkänen M., Mäenpää T., Vertola J., Kyllönen J., Huovnen S., Outex-new framework for emprcal evaluaton of texture analyss algorthms, In: Proc. 16th Int. Conf. on Pattern Recognton, vol. 1, pp: , 00(b). 40

6 Internatonal Journal of Computer Applcatons ( ) Volume 55 o.1, October 01 [15] Antono Fern andez, Ovdu Ghta, Elena Gonz alez, Francesco Bancon, Paul F. Whelan, Evaluaton of robustness aganst rotaton of LBP, CCR and ILBP features n grante texture classfcaton, Machne Vson and Applcatons, 011. [16] Xaoou Tang, Texture Informaton n Run-Length Matrces, IEEE, pp: , [17] Ramana Reddy B.V., Radhka Man M. Sujatha B., Vjaya Kumar V., Texture Classfcaton Based on Random Threshold Vector Technque, Internatonal Journal of Multmeda and Ubqutous Engneerng Vol. 5, o. 1, January,