A Novel Adaptive Descriptor Algorithm for Ternary Pattern Textures

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1 A Novel Adaptve Descrptor Algorthm for Ternary Pattern Textures Fahuan Hu 1,2, Guopng Lu 1 *, Zengwen Dong 1 1.School of Mechancal & Electrcal Engneerng, Nanchang Unversty, Nanchang, , Chna; 2. School of Mechancal & Electrcal Engneerng, Jangx Unversty of Scence and Technology, Ganzhou, , Chna Abstract Tradtonal Local Bnary Patterns (LBP) and Local Ternary Patterns (LTP) algorthms suffer from: ) the threshold cannot be determned adaptvely, ) low texture descrpton resoluton, and ) only the unform mode can be encoded. In ths paper we propose a local ternary patterns texture descrptor algorthm whose threshold s determned adaptvely, and s termed: OECS-LATP: Odd-Even parallel Center-Symmetrc Local Adaptve Ternary Pattern. Frst, the threshold s determned adaptvely accordng to the varaton of mage gray level. Secondly, the center-symmetrc and odd-even parallel desgn are adopted n the encodng operaton. Not only the contrast of gray-levels between two symmetrc pxels wth the center pxel s determned but also the contrast of gray-levels between the center pxel and local neghborhood pxels are consdered, thus the local texture feature s depcted more effectvely. Then the proposed algorthm s compared wth LBP, LTP and SILTP algorthms through Brodatz dataset and CUReT dataset. The expermental results show that based on these two datasets, the proposed algorthm surpasses LTP n precson by 6% and 21% respectvely, and has great mprovement n: ) recognton rate, ) stronger ant-nterference ablty and ) s more robust under the condton of uneven llumnaton than tradtonal LTP and SILTP descrptors. Key words - texture feature; local ternary patterns; adaptve threshold; mage retreval I. INTRODUCTION Image feature representaton and acquston s the bass for the computer vson and mage processng technque. Generally, the mage features nclude color, texture, and shape etc, whle texture feature s very crtcal for mage analyss and processng. How to acqure mage texture feature nformaton has been a hot topc n mage feature extracton [1]. Generally, texture s the specal pattern composed of perodc or random basc component [2], and t has the characterstc of mcroscopc rregular and macroscopc regular [3]. Extracton of mage texture feature s wdely used n computer vson, e.g., mage segmentaton [4], machne vson [5] and patter recognton [6]. Recently, the texture analyss method can be dvded nto 4 categores [7]: structure-based method, statstcs-based method, spacedoman/frequency-doman based method, and jont modelng method. For structure-based method, t focused on how to descrbe the nternal relatonshp and algnment rules among the basc texture components, thus t was generally appled n mage texture analyss and processng due to the smple computaton and rotatonal nvarant. LBP (Local Bnary Patterns) and LTP [8] (Local Ternary Patterns) were two typcal examples of structure-based method. The texture descrpton method of LBP was frstly proposed by Ojala [9]. For ths method, both the structure features and statstcal features were consdered, and t has the advantage of smple computaton and nsenstve to objectve gray varaton, thus t was wdely appled n mage matchng [10], mage retreval [11], and medcal mage analyss [12]. Based on the work of Ojala, the LBP operator s mproved by dmensonalty reducton wth Unform LBP n [13], and the advantage s that the computaton s reduced, whle t can only appled to the case of U>2. In [14], MB_LBP (Mult-Blok Local Bnary Pattern) s taken and the gray value of sngle pxel s substtuted by the average gray of the sub-regon, thus t mproves the ant-nterference ablty, whle reducng the mage resoluton and recognton rate. For the tradtonal LBP texture operator, the texture resoluton was not very hgh snce t takes the bnary pattern and mostly used to descrbe the texture spatal features. In order to mprove the texture descrpton resoluton, the LTP was proposed by Tan [15], and LTP method was further mproved by Chen [16]. Whle the local ternary pattern was drectly appled n the encodng, t wll result n large gray-level range and degrade the system real-tme performance and effcency. SILTP (Scale Invarant Local Ternary Pattern) s taken n to extract the mage features [17]. Although t partly mproves the ant-nterference ablty, the gray value of central pxel s only consdered whle not the regon value when settng the threshold, and thus results n weak robustness. In ths paper, based on the exstng dsadvantages of the tradtonal LBP and LTP mode, the LTP operator s mproved, and OECS-LATP (Odd-Even parallel Center- Symmetrc Local Adaptve Ternary Pattern) operator s desgned, whch determnates the threshold n an adaptve DOI / IJSSST.a ISSN: x onlne, prnt

2 way to enhance the robustness. Furthermore, to make up the dsadvantage that non-unform mode was neglgent n tradtonal LTP, the center-symmetrc and odd-even parallel are adopted n encodng operaton, whch wll greatly mprove the texture descrpton resoluton and also consder the case of non-unform mode. In ths paper, the prevous works on LBP and LTP algorthms are ntroduced and the drawbacks of LBP algorthm and features of LTP algorthm are analyzed n Secton-II. Based on the features of LTP algorthm, the optmal threshold s automatcally determned by takng the adaptve ternary method n Secton-III. In Secton-IV, OECS-LATP descrpton operator s desgned to overcome the drawbacks of tradtonal descrpton operator, and t also mproves the effectve descrpton ablty of mage texture. In Secton-V, the experments are performed and results are analyzed, the performance of the proposed algorthm s verfed through 3 groups of experment. Fnally, the conclusons are drawn n Secton-VI. II. LBP OPERATOR AND LTP OPERATOR DESCRIPTION A. LBP Operator The prncple of LBP texture descrpton s to select a local neghborhood for each pxel (center pxel), and compare the gray-level of the center pxel wth other pxels n the local neghborhood [18]. If the gray-level values of any local neghborhood pxel s lower than the center pxel, the value of ths pxel wll be set to 0, otherwse t wll be set to 1, and the LBP value of ths center pxel s obtaned by multplyng the assgned values wth correspondng weghts and addng the results together. Assume that the gray-level value of center pxel s p c, and the pxels of local neghborhood s p, then the LBP value of pxel (x, y) can be represented as: N 1 R, N x, y s( p ) 2 0 LBP (1) 1, x 0 s( x) (2) 0, other Where R s the radus of local neghborhood, N s the number of pxels n the local neghborhood. Generally, the sze of local neghborhood s selected as 3 3 (pxels), thus there are 8 pxels around the center pxel, and the 8-bt bnary encoder s obtaned, whle the pxel gray-scale s 256 (8-bt). Takng the gray-level of 8-neghorng area for example, the process are showed n Fg.1(a). The bnary sequence after threshold comparson s , and the (a) The calculaton process of LBP (b) The LBP value of neghborhood. Fgure 1. The LBP comparson of two local regons. LBP value s 135 after decmal converson. The hstogram of the entre mage can be acheved from the LBP value of each pxel, and thus the feature vector could be extracted. From the above computaton, t s notced that LBP operator has the advantage of rotatonal nvarant, balanced contrast and smple computaton, however, there are also some dsadvantages for LBP, e.g., low texture descrpton resoluton, and relable to center pxel, t only compare the center pxel wth ts neghborhood pxel, whle neglectng the relaton among the neghborhood pxels. Thus sometmes although there s large varaton for the pxel n the local neghborhood, the encoder s nvarant. Take a look at the local neghborhood gray-level n Fg.1(b), t s notced that gray-level s very dfferent from Fg.1(a), whle after LBP operaton, the LBP value s 135, equvalent to the LBP of Fg.1(a). Consequently, tradtonal LBP operator cannot effectvely descrbe the dfference before and after nonlnear transformaton, t wll result n some sgnfcant texture feature loss. B. LTP Operator In order to overcome the defcency of low resoluton and weak ant-nference of LBP operator, LTP operator s mproved based on LBP operator [19], Ternary encodng s taken for LTP operator, and 0, 1, 2 are set n the operator quantzaton stage, wth the threshold t defned by user. The pxel dfference n the range of [-t,t] s mapped n to 1, dfference larger than t s mapped n to 2, and less than t s mapped to 0, then s( p - p c ) wll be transformed nto the form of ternary encodng. The specfc form s expressed as: DOI / IJSSST.a ISSN: x onlne, prnt

3 2 ( p ) t s( p ) 1 p t (3) 0 ( p ) t Where p s the gray-level of neghbor s pxel, and p c s graylevel of center pxel, t s the threshold defned by user, thus the encodng form for the weghts s: the sample nfluence. The process of threshold t computaton s as follows: (1) Compute the gray contrast value of center pxel and neghborhood pxel n the range of (R, N): Δp = p - p c. (5) (2) Compute the mean of the gray contrast values: N 1 R, N ( x, y) s( p 0 LTP p ) 3 c (4) = ( p ) N (6) p N 1 0 / LTP operator retans the advantages of LBP operator to effectvely express the local mage texture, pattern, mcrofeature and mprove the texture descrpton resoluton. In addton, t greatly mprove the ant-nference ablty by varyng the threshold t, and act as an equalzer under the condton of uneven llumnaton. However, the value of t has great effect on the overall performance for LTP operator, thus the choosng of t s very crtcal. Snce t s the threshold defned by user, and a certan threshold can not be appled to all the samples, thus t s necessary to fnd a way to automatcally determnate the threshold t adaptve to the mage varaton, to further mprove the performance of LTP. III. ADAPTIVE TERNARY PATTERN OPERATOR In order to solve the problem of determnng threshold t dffcultly, an adaptve threshold method s proposed n ths paper. The prncple of dynamcally generatng the threshold t s based on the degree of dsperson of neghborhood gray contrast value,that s LATP (Local Adaptve Ternary Pattern). Snce the threshold t s generated dynamcally, t can be appled to dfferent samples, and thus solve the problem of unversal adaptablty. The standard devaton σ n Data Statstcs reflects the data dstrbuton [20]. The smaller the σ value s, the more concentrated the data dstrbuton s; vce versa, the larger value of σ denotes that the data dstrbuton s dscrete wth large fluctuaton, so the standard devaton σ s set as the threshold t n ths paper. In the neghborhood doman of (R, N), the degree of dsperson σ on the gray contrast value between neghborhood pxels and center pxel reflects the varaton of the pxel gray values n ths regon. Consequently, n ths paper, the standard devaton σ s taken as threshold t, and the threshold t s dynamcally determned accordng to the varaton of contrast value between the center pxel and neghborng pxels, n order to strengthen the mage texture feature descrpton and reduce (3) Compute the varance of the gray contrast values: D = ( N 1 0 (4) Compute the threshold t: 2 ( p p) ) N (7) / t D (8) From the above process, t s notced that the threshold t reflects the degree of dsperson of samples. The threshold t s automatcally obtaned wth the ad of statstcal method, and t vares wth the changng of mage gray-level, thus can appled to dfferent samples. Fg.2 shows an example of LATP algorthm for a local neghborhood, the threshold t s adaptvely ganed frstly, and then LTP ternary encodng s performed. Fgure 2. The threshold computaton of LATP operator. For the LATP method, the optmal threshold t s determned by the varance of neghborhood pxels, and t can adapt to the llumnaton dfference of dfferent area n an mage. The fxed threshold s replaced by dynamc threshold, thus t s adaptve. LATP mantans the advantages of the tradtonal LTP, and also optmze the threshold t, thus t solve the problem of threshold varaton ntra-sample and nter-sample, mprove the texture descrpton resoluton and robustness. IV. DESIGN OF OECS-LATP DESCRIPTION OPERATOR Snce ternary encodng s taken for LATP operator, the texture descrpton resoluton s hgher than LBP, however, the descrpton dmenson s also larger (3 N ), t wll greatly ncrease the computaton complexty, and affect the real-tme DOI / IJSSST.a ISSN: x onlne, prnt

4 performance. Take the neghborhood of (1, 8) for example, the gray-level range for LATP s LATP 1,8 =3 8 =6561, whle the LBP s LBP 1,8 =2 8 =256, so the computatonal complexty of ternary encodng s obvously hgher than LBP. For ths case, there are 2 ways to reduce the computatonal complexty, one s the mode of rotatonal nvarance taken by Zhu [21], 24 modes wth unform values of 0, 2, 3, or 4 s taken for computaton. Although the dmenson and computatonal complexty are reduced, t neglected the case that when the Unform value s not 0, 2, 3, or 4. The other way taken s to dvde ternary nto postve and negatve modules, and the 0, 1, 2 ternary mode s replaced by -1, 0, 1: 1 ( p ) t s( p ) 0 p t (9) 1 ( p ) t And then the ternary encodng s decomposed nto postve and negatve modules. For the postve module, -1 n the orgnal code s changed to 0, and the other values keep nvarant, whle for the negatve module, 1 n the orgnal code s changed to 0, -1 changed nto 1, and the other values keep nvarant. Fnally a group of bnary number s obtaned by takng the OR operaton on these two modules. For example, an mage after threshold s 1100(-1)(-1)00, the specfc procedure of ths method s llustrated n Fg. 3. Actually ts objectve s to decompose the orgnal ternary code nto two bnary LBP codes. The dmenson and computaton complexty s reduced, but -1 s changed nto 1, thus -1 s equvalent to 1, and the encodng process s: 1 s( p ) 0 1 ( p p ) t p p t ( p p ) t c c c It could decrease the feature dmenson and computatonal complexty, whle the texture nformaton s effectvely descrbed. The procedure for OECS-LATP descrpton operator s as follows: N / s( p p N / 2) (10) 0 H 3 N /2 1 2 N/2 1 s( p2 p c ) + ( 0 0 H 3 LTP N R, H1 H 2 s p 2 1 p c ) 3 (11) (12) Where s(x) s the weghtng parameter wth: 2 x t s( x) 1 x t (13) 0 x t Where t s the adaptve threshold, and obtaned as Equaton(5)~(8). Fg. 4 shows the process of encodng, the descrpton operator s conssts of H 1 and H 2, H 1 s to descrbe the relatonshp between the two center symmetry pxels of the center pxel. It ndcates the mult-level dfference of the symmetry pxels n a neghborhood, and t can also replace the gradent operator to descrbe local graylevel dstrbuton. The extracted features by H 1 ndcates the gray-level varaton of local mage area, and also have better ablty of ant-nterference to nose. H 2 s to descrbe the relatonshp between the neghborhood pxels and the center pxel. It ndcates the varaton between the neghborhood So the resoluton and performance of ternary encodng wll be greatly affected. Fgure 4. The algorthms of OECS-LATP. pxels. In order to reduce the computatonal dmenson, the odd and even pxels are computed n parallel n the H 2, thus the mage texture varaton s descrbed and computaton complexty s reduced, the max dmenson s =243. Fgure 3. The calculaton of postve and negatve modules In ths paper, the Odd-Even parallel Center-Symmetrc Local Adaptve Ternary Pattern (OECS-LATP) s proposed. DOI / IJSSST.a ISSN: x onlne, prnt

5 V. EXPERIMENT RESULTS AND DISCUSSION A. Brodatz Expermental Dataset and CUReT Expermental Dataset In ths paper, n order to valdate the performance of OECS-LATP operator, Brodatz mage texture set wth even llumnaton( 3997) and CUReT mage texture set wth uneven llumnaton( uret) are selected for testng, and the testng performance s compared wth LBP and LTP. Brodatz mage texture set s generally regarded as typcal mage texture set, whch nclude 111 knds of natural texture mage, 24 knds of even texture mage, and 87 knds of uneven texture mage. The sze of each mage s (pxels), several even texture pctures are shown n Fg. 5(a), and some uneven texture pctures are shown n Fg. 5(b). Snce there s only one mage for each knd of Brodatz mage texture set, so each mage s segmented nto 16 sub-mages of (pxels) for detecton testng n ths paper. The Fg. 6 shows mage D42 after segmentaton, 10 sub-mages of whch are used for tranng set, and the other 6 are used for testng set. In order to fully measure the algorthm performance, Brodatz mage texture set s dvded nto 2 parts for testng: Part A1 nclude 24 knds of even texture sets (384 sub-mages), and Part A2 nclude 87 knds of uneven texture sets (1392 sub-mages). The testng wll repeat for 50 tmes and take the average as the fnal results. Compared to Brodatz mage texture set, CUReT ( Columba-Utrecht Reflectance and Texture set) s more complex. It ncludes 61 knds of texture mages. Each knd of mages nclude 92 mages of same object n dfferent lghtng condtons or dfferent shootng angles, so there s large dfference between mages even n the same knd of mages, thus t s generally consdered as texture database wth great challenges. Part of CUReT mages are shown n Fg. 7, some pctures of same object n dfferent lghtng condtons are lsted n the frst row, and some of dfferent knds of mages are lsted n the second row. The sze of each mage s (pxels). Because CUReT mages are very large, 30 knds of mages are selected from 61 knds of mages for testng, and then 20 mages are selected out for each knd of mages. (b) some examples of uneven texture mages (A2) Fgure 5. Some examples of Brodatz mages. Fgure 6. The 16 sub-mages cut from D42. Fgure 7. Some examples of CUReT mages. For each mage, t s splt nto 12 sub-mages wth sze of (same sze as sub-mage n Brodatz mage texture set), and then 8 sub-mages are selected as tranng set, and 4 mages are selected as testng set. The total number of submages for testng s The testng procedure wll repeat 50 tmes, and take the average as the fnal results. DOI / IJSSST.a ISSN: x onlne, prnt

6 B. Threshold t Settng and Detecton Classfcaton crteron In ths paper, OECS-LATP operator s utlzed to extract the mage texture features, and the frst step s to adaptvely determnate the threshold t. In theory, t needs to recalculate the threshold t for each pxel. In the actual process, there s no necessary to calculate the threshold t for each pxel when consderng the system computaton complexty and actual mage varaton. We splt an mage to be detected nto several blocks, the threshold t for the center pxel of each block wll be obtaned as Equaton (5)~(8), and the threshold t would be set as the threshold for whole block. Based on the computaton complexty and mage gray-level varaton, the sze of each block s set as (pxels) n ths paper. The nearest-neghbor classfer s used for retreval, and the smlarty for two mages s measured by the Eucldean dstance n texture hstogram: ( h1 h2 ) X ( H1, H 2) (14) ( h h ) Where H 1 H 2 are the texture hstograms for two mages respectvely, h 1 h 2 are the mage gray- levels respectvely. The detecton performance s measured by precson rate and recall rate. C. Experment Result Dscusson In order to better valdate proposed algorthm, the performance of LBP, tradtonal LTP, SILTP and OECS- LATP algorthm was compared. The process of experments s as follows: (1) The parameter settngs for the experments are: for the experments n ths paper, 8-neghborhood computaton s selected, that s the radus of neghborhood R = 1, and the neghborhood pxel number N = 8. The threshold t for LTP algorthm s set as 20, whle The threshold t for SILTP algorthm s set as 0.1 p c. The sze for each block n OECS-LATP operator s pxels. Testng hardware settng are CPU-Pentum, basc frequency 3.0 Ghz, Memory 2.0 GB, and the programs are wrtten n Vclanguage. (2) Accordng to the method n secton 4.1, the selected mages from Brodatz and CUReT sets are splt nto submages wth sze of , and then these sub-mages wll be dvded nto tranng set and testng set respectvely. Fnally all the sub-mages wll be transformed nto correspondng texture mages based on the algorthm of LBP, LTP, SILTP and OECS-LATP. (3) The texture hstogram of the sub-mages n the tranng set from Brodatz and CUReT sets s extracted as feature vector. (4) The texture hstogram of the sub-mages n the tranng set s extracted as feature vector, and then the mage retreval s performed accordng to Equaton (14), thus we can search out the orgnal mage categores for the mages n the testng set, and calculate out the results for LBP, LTP, SILTP and OECS-LATP algorthms. (5) After repeated experments, the average values are taken as fnal results, the testng performance for part-a1 and A2 n Brodatz texture set and CUReT set s demonstrated n Fg. 8. Precson Precson Precson LBP LTP SILTP OECS-LATP Recall (a) the results of A1 texture mages. LBP LTP SILTP OECS-LATP Recall (b) the results of A2 texture mages. LBP LTP SILTP OECS-LATP Recall (c) the results of CUReT texture mages. Fgure 8. Performance comparson among four algorthms. DOI / IJSSST.a ISSN: x onlne, prnt

7 In order to further analyze the nfluence of sze of block on the performance wth OECS-LATP operator, 1 1 (pxels), (pxels) and (pxels) are selected as the sze of block for testng. The testng results on CUReT mage texture set are lsted n Table-1. From Fg. 8, t s notced that for the retreval recognton of A1 mage set, the average precson s very hgh (above 85%) wth mnor dfference for all four algorthms, because the mage texture n A1 s even, and the TABLE-1 PERFORMANCE COMPARISON FOR DIFFERENT ALGORITHMS Average Retreval Precson (%) Tme (ms) LBP LTP SILTP OECS-LATP(1 1) OECS-LATP(10 10) OECS-LATP(20 20) dfference of all the sub-mage after segmentaton s small, thus small dfference between testng samples and tranng samples. Consequently the retreval precson s relatvely hgh. Whle for the A2 mage set, the retreval precson of 4 algorthms are a lttle lower compared to that of A1, because the rregularty of the uneven texture n A2 results n great dfference n the 16 sub-mages after segmentaton. From Fg. 6, t s notced that after segmentaton there are sgnfcant dfference among the sub-mages, thus wll cause the great dfference n testng samples and tranng samples, and affect the performance. Fortunately, OECS-LATP algorthm takes the adaptve ternary encodng n ths paper, t not only consders the relatonshp between two symmetry pxels of the center pxel, and also the relatonshp between neghborhood pxels and center pxel when descrbng the texture features, thus the detecton precson has mproved approxmately 6% compared wth LBP and 4% wth the SILTP. Consequently, the performance s mproved under the condtons of some nformaton loss for OECS-LATP algorthm. For CUReT mage texture set, each knd of mages are several mages of same object n dfferent lghtng condtons or dfferent shootng angles, thus the accuracy of 4 algorthms are all lower than prevous experments. The largest decreasng degree s from LBP, the hghest precson s 62%, and average accuracy s 47%. Whle for the OECS- LATP algorthm n ths paper, the hghest accuracy s 83%, and average precson s 70%, 23% hgher than LBP, 21% hgher than LTP, and 12% hgher than SILTP, thus the detecton performance of OECS-LATP algorthm s obvously better than the other 3 algorthms. Snce LBP and LTP take the fxed threshold of gray-level, and they are senstve to uneven lghtng llumnaton, so t wll result n lower detecton precson. Although the threshold for SILTP vares as the change of mages, t vares based on the central pxel wthout consderng of the neghborng pxels, thus the detecton performance s not very good. However, for OECS-LATP algorthm n ths paper, the optmal threshold t s adaptvely determned based on all pxels n the regon and ternary encodng s taken to mprove the resoluton. In addton, t not only consders the relatonshp between two symmetry pxels of the center pxel, and also the relatonshp between neghborhood pxels and center pxel when desgnng the descrpton operator. Consequently, t has hgher resoluton and robustness under the condtons of uneven lghtng llumnaton and partal nformaton loss. In order to further valdate our proposed algorthm, a texture mage taken by author s transformed nto local texture mages, and the parameters settng are same as the above Fgure 9. Orgnal mage and texture mages for dfferent algorthms. experments. The results are shown n Fg. 9. The orgnal mage n the fgure s under uneven lghtng llumnaton, we notced that there are also uneven brght ponts after LBP and LTP algorthm, and ths phenomenon s lttle mproved wth SILTP algorthm, whle t s greatly mproved wth the OECS-LATP algorthm n ths paper. From Table-1, the sze of block has great effect on the performance of OECS-LATP algorthm. The smaller the block s, the more reasonable the threshold t s, and the testng accuracy wll be hgher, whle t wll last for longer tme, thus t s better to make a trade off between accuracy and tmelness. From the above experments, t s concluded that the man advantages for the OECS-LATP algorthm n ths paper are applcatons n the cases of uneven lghtng llumnaton and partal nformaton loss. VI. CONCLUSIONS In ths paper, OECS-LATP s proposed to overcome the dsadvantages of the tradtonal LPB and LTP algorthm: DOI / IJSSST.a ISSN: x onlne, prnt

8 threshold s not automatcally determned, texture descrpton resoluton s not very hgh, and only Unform mode s encoded. OECS-LATP takes ternary encodng to mprove the texture descrpton resoluton. In addton, the optmal threshold t s adaptvely determned based on the varance of local gray-level varaton, thus even mages can be obtaned under large varaton llumnaton condton. Fnally, the descrpton operator not only consders the relatonshp between two symmetry pxels of the center pxel, and also the relatonshp between neghborhood pxels and center pxel, thus the texture nformaton can be fully captured, and the system computaton complexty s lowered. Consequently, compared to the tradtonal LBP, LTP and SILTP algorthm, the OECS-LATP algorthm proposed n ths paper greatly mprove the texture resoluton and robustness under the condtons of partal nformaton loss or uneven lghtng llumnaton. One drawback of the proposed operator s that the mage s dvded nto several blocks n rgd way, and then the threshold t for each block s adaptvely determned. Hence n the future work, we wll focus on how to dvde the mage nto several blocks n flexble way based on the varaton of mage texture, and then adaptvely determne the threshold t for each block, thus the performance of the proposed algorthm wll be further mproved. ACKNOWLEDGEMENTS The authors thank the revewers who gave a through and careful readng to the orgnal manuscrpt. Ther comments are greatly apprecated and have help to mprove the qualty of ths paper. The authors also thank the Natonal Natural Scence Foundaton of Chna( ) for fnancal support. REFERENCES [1] K. C. Song, Y. H. Yan, W. H. Chen and X. Zang, Research and Perspectve on Local Bnary Pattern, Acta Automatca Snca, vlo.39, no.6, pp , [2] S. Ma, K. Lu and G. 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