Thangka Image Retrieval System Based on GLCM. Shou-liang TANG, Jian-bang JIA, Chuan-qian TANG, Xiao-jing LIU * and Zhi-qiang LIU

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1 017 Internatonal Conference on Electronc, Control, Automaton and Mechancal Engneerng (ECAME 017) ISBN: Thangka Image Retreval Sstem Based on GLCM Shou-lang TANG, Jan-bang JIA, Chuan-qan TANG, Xao-ng LIU * and Zh-qang LIU Qngha Unverst, 51 Nng Da Road, Chengbe Dstrct, Xnng, Qngha rovnce, Chna *Correspondng author Kewords: Thangka, Teture, GLCM, Statstcal features. Abstract. Thangka Image Retreval Sstem s a challengng task n Thangka mage classfcaton. The Thangka mages obtaned from dfferent was are not all true Thangka mages, whch ma lead to the msclassfcaton of Thangka mages. Therefore, a retreval sstem s needed to dstngush true and false Thangka mages. Thangka has rch teture characterstcs, so ths paper proposes a knd of feature etracton based on gra level co-occurrence matr (GLCM) algorthm, to etract the statstcal features of the matr of thangka mage retreval, the dstncton between has obtaned the good effect. Background Introducton Thangka s a tradtonal art treasures of the Chnese naton, wth the characterstcs of fne, subtle and comple structure nformaton. How to quckl and accuratel retreve the mages of Thangka from the Thangka mage lbrar has mportant research sgnfcance. Based on the comple features of Thangka mage teture, t s of practcal sgnfcance to use Thangka mage retreval based on teture. Now teture measurement methods can be broadl dvded nto two categores: statstcal analss and structural analss [1]. The former starts from the statstcal analss of the related attrbutes of the mage, and the latter starts wth the teture prmtves and eplores the structural rules of teture composton. In terms of hstorcal development or current progress, statstcal methods stll domnate. GLCM s a commonl used statstcal method n teture analss. In ths paper, a Thangka mage retreval sstem based on GLCM s desgned. Analss of the rncple of GLCM GLCM s a knd of analss method whch uses the spatal correlaton of two pels separated b a certan dstance n the mage space to reflect the arrangement pattern of the gra space n the mage []. It reflects the ntegrated nformaton of mage gra wth respect to drecton, adacent nterval and change range, whch las the foundaton for analzng the global pattern and arrangement rules of mages. The selecton of the step parameters and the drecton parameters has a great nfluence on the calculaton of the GLCM, whch s drectl related to the pros and cons of the generated teture features. Generall, the step parameters are selected frst, and the selecton drecton s generall 0 o, 45 o, 90 o and 135 o. However, due to the smmetr of the gra level co-occurrence matr, the drectons are actuall 8 drectons. There are 4 co-occurrence matrces avalable. In order to facltate the analss, matr elements commonl used to represent the probablt value, that s, each element (, dvded b the sum of each element R, a normalzed value (, than 1 s obtaned: (, (, = R If the sze of the mage s M N, then [ 0, M 1], [ 0, N 1] 58. less (1)

2 However, GLCM s not drectl used as the dstngushng feature of the teture. Instead, some correspondng egenvalues calculated b GLCM are used as the teture classfcaton features. Snce 1973, Haralck et al. have defned 14 gra level co-occurrence matr characterstc parameters for teture analss [3], however, some egenvalues are consstent wth each other, whch knd of teture features are more effectve for the Thangka mage retreval. At present, there are stll few studes at home and abroad. Therefore, accordng to the mage features of Thangka, fve of them are selected as the egenvalues of teture recognton. The are energ, entrop, correlaton, moment of nerta (or contrast), and local statonar (or nverse dfference moment). 1) Energ Asm = (, () Energ n the gra-scale co-occurrence matr s ver mportant, s a measure of the unformt of the mage, reflectng the unformt of the grascale dstrbuton and teture of the thckness [4]. If the mage s more unform, the more rough the teture, the greater ts value. ) Entrop Ent = (, log (, (3) Entrop s a measure of the randomness of the mage teture, whch reflects the complet of the teture of the mage. The greater the entrop s, the more comple the mage s [5]. 3) Correlaton Corr = µ = µ = σ ( (, (, (, σ σ = µ, ( ) ( µ µ (4) (5) (6) (7) σ ( ) ( = µ, (8) Measure the degree of smlart n the grascale or column drecton of the mage, so the sze of the value reflects local relevance, and the larger the value, the greater the correlaton. 4) Moment of nerta(or contrast) Compared wth other statstc moments of nerta, the complet of spatal dstrbuton of mage gra can be dstngushed more. Its mathematcal defnton s: ( ( Con =, (9) 59

3 Therefore, the value of the moment of nerta can reflect the grascale complet of the mage. The larger the value of the mage, the more obvous the mage groove s, the clearer the mage s. 5) local statonar (or nverse dfference moment) (, ( ) IDE = 1 + (10) It reflects the unt of the mage teture, the value of the large mage llustrates the lack of changes n dfferent regons of the teture [8]. Algorthm and Techncal Analss Algorthm to Acheve the rocess The algorthm uses the C++ programmng, the algorthm mplementaton process as shown n Fgure 1: Grascale Compresson Fgure 1. Algorthm mplementaton process. In practce, the gra level of an mage s generall 56 levels, and the sze of a co-occurrence matr s [9]. Snce Thangka gra scale mages and more large sze, whch n turn requres the algorthm to calculate pel b pel, so wll nevtabl lead to unusuall large amount of computaton. Therefore, wthout affectng the teture feature etracton, the gra level of the orgnal mage s usuall compressed to a smaller range n order to reduce the sze of the generated gra level co-occurrence matr and dvde the mage nto several sub-wndows [10], mprove the effcenc of the algorthm. The stud desgned to acheve the man use of the eght gra-scale compressons. Epermental Results and Analss The Thangka mages used n the eperments are all processed. As shown n Fgure, t can be seen that the mage teture nformaton s rch n nformaton of the mage teture through the epermental results. The outlne, edge nformaton, drecton nformaton and other teture performance has a good effect. 60

4 Fgure. The epermental nterface. Fgure 3. The epermental results. Dfferent Thangkas have dfferent teture features. Snce these features represent some aspect of the Thangka mage, comparng these values can reflect the dfference of Thangka mage teture features, t s feasble to use t as a feature of the search. In the epermental results, we can see that the epermental results can be ver good to fnd the mage of the thangka n the mage lbrar. Concluson Ths stud n C++ programmng to acheve the thangka mage retreval sstem based on gra level co-occurrence matr, through comparatve analss of thangka mage processng results, t s concluded that the energ, entrop, correlaton, moment of nerta (or contrast) and local statonar (or nverse dfference moment) for thangka mage retreval s a characterstc value of adaptablt, stablt s ver good. As an mportant parameter n Thangka mage retreval, teture features pla an mportant role n the accurac of the results. Therefore, these fve egenvalues have ver mportant applcaton value. At the same tme there are man defcences n the sstem, such as the need to manuall enter the error range of the 5 parameter values, ths wll make the sstem appear ver tedous, s not conducve to the accurac of the upgrade. We can use the machne learnng method, the statstcal dstance, the number of compresson seres, the range of egenvalue error, etc., to effcentl and quckl retreve 61

5 the mage of the thangka n the mage lbrar, and t can also be mproved from the algorthm to reduce the amount of computaton, these wll be the net step of the stud. Acknowledgement Ths paper s supported n part b Scence and Technolog roect n Qngha rovnce (No.015-ZJ-75), "The roect of Qngha Scence & Technolog Department" (No: 016-ZJ-Y01) and "The Open roect of State Ke Laborator of lateau Ecolog and Agrculture, Qngha Unverst" (No:017-ZZ-16). References [1] Sastr S S, Kumar T V, Rao C N, et al. Transton Temperatures of Thermotropc Lqud Crstals from the Local Bnar Gra Level Cooccurrence Matr[J]. Advances n Condensed Matter hscs, 01, 01(01): [] Squera F R D, Schwartz W R, edrn H. Mult-scale gra level co-occurrence matrces for teture descrpton[j]. Neurocomputng, 013, 10(10): [3] Haralck R M. Teture features for mage classfcaton[j]. Sstems Man & Cbernetcs IEEE Transactons on, 1973, smc-3(6): [4] Benco M, Hudec R. Novel Method for Color Tetures Features Etracton Based on GLCM[J]. Radoengneerng, 007, 16(4): [5] Kekre H B, Thepade S D, Sarode T K, et al. Image retreval usng teture features etracted from GLCM, LBG and KE[J] [6] Mohanaah, Sathanaraana, Gurukumar L. Image teture feature etracton usng GLCM approach[j] [7] Zulpe N S, awar V. GLCM tetural features for Bran Tumor Classfcaton[J]. Internatonal Journal of Computer Scence Issues, 01, 9(3). [8] Ehsanrad A, Sharath K Y H. Leaf recognton for plant classfcaton usng GLCM and CA methods[j]. Orental Journal of Computer Scence & Technolog, 010, 3(3):6. [9] Xu G, L X, Su J, et al. A Method for the Estmaton of the Square Sze n the Chessboard Image usng Gra-level Co-occurrence Matr[J]. Measurement Scence Revew, 01, 1(): [10] Sucat N, Herumurt D, Waa A Y. Feature etracton usng gra-level co-occurrence matr of wavelet coeffcents and teture matchng for batk motf recognton[j]. roceedngs of the Spe, 017, 5: