SEPARATION OF ORIGINAL PAINTINGS OF MATISSE AND HIS FAKES USING WAVELET AND ARTIFICIAL NEURAL NETWORKS

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1 ISTANBUL UNIVERSITY JOURNAL OF ELECTRICAL & ELECTRONICS ENGINEERING YEAR VOLUME NUMBER : 2009 : 9 : 1 ( ) SEPARATION OF ORIGINAL PAINTINGS OF MATISSE AND HIS FAKES USING WAVELET AND ARTIFICIAL NEURAL NETWORKS Baybora Temel 1 Nyaz Klc 2 Bunyamn Ozgultekn 1 Osman N. Ucan 2 1 Marmara Unversty, Educaton Scence Insttute, Fne Arts Dept. Goztepe, Istanbul, Turkey bayboratemel@hotmal.com 2 Istanbul Unversty, Engneerng Faculty, Electrcal and Electroncs Eng. Dept , Avclar, Istanbul, Turkey ABSTRACT In recent years, wth the latest developments n computer technology, wavelet and Artfcal Neural (ANN) are beng wdely used n dfferent felds and dscplnes. Especally, wavelet followed by ANN applcatons has produced successful results n mage processng. In ths paper, we have appled wavelet followed by ANN to obtan an objectve approach n seperatng orgnal pantngs of Matsse and fakes. In art envronment, the term of fake can be explaned as producng new pantngs resemblng to the artst s pantng style. The works of Matsse have been especally chosen snce hs pantngs are mostly faked. Here, wavelet s utlzed for feature extracton of 2D pantngs. Thus, mportant propertes of nput mage are extracted whle reducng nput parameters wth mnmum loss of nformaton. ANN s then appled n separaton process between pantngs. At the end of the overall separaton task, we obtaned 88 % classfcaton accuracy. Keywords: Henr Matsse, Pantng separaton, Wavelet transform, Artfcal Neural 1. INTRODUCTION In recent years, many mage processng algorthms are started n evaluaton of 2D pantngs as new developments are nvolved n computer technology. There are many studes about classfcaton problem n 2D mages [1-4]. One of the man problems n art researches s the separaton of pantngs wth smlar crtera.e. same art movement, same century, masterpeces, fakes and vsual content etc [1, 2]. Another man problem n art researches s art forgery. Art forgery refers to creatng and, n partcular, sellng works of art that are falsely attrbuted to be work of another, usually more famous artst. Here are a few avalable technques used aganst art forgery. The frst and commonly use of them s carbon-14 datng used to measure the age of an object up to 10,000 years old [5]. The other s Whte Lead datng used to determne the age of an object up to 1,600 years old [6]. To detect the work of a sklled forger, nvestgators must rely on other methods such as dgtal authentcaton. Usng the technque called wavelet decomposton can be a new method to detect forgeres. Usng wavelet decomposton; a pcture s broken down nto a collecton of more basc mages called sub-bands. Receved Date: Accepted Date:

2 792 Separaton of Orgnal Pantngs of Matsse And Hs Fakes usng Wavelet and Artfcal Neural These sub-bands are analyzed to determne textures, assgnng a frequency to each sub-band. Then these data are used as nput nto Artfcal Neural for testng. For ths testng, the works of Matsse have been used snce ts hgh rato of fakes. He was born n 1869 at Northern France. He obtaned a dploma n law and worked as a lawyer's assstant for a perod of tme. In 1896, he exhbted four pantngs at the Salon de la Socete Natonale and sold two of them [7]. In ths paper, Matsse and hs fakes are separated by two consecutve approaches; wavelet and ANN n evaluaton of hgh capacty 2D pantng mages. In hgh resoluton mages, one step drect mage processng s almost mpractcal because of tme consumng long teratons. The frst approach s based on compresson wth mnmum loss va feature extracton. The second s classfcaton of orgnal Matsse pantngs and hs fakes by ANN, where extracted features are taken as nput. We have proposed a supervsed classfer to construct and evaluate Matsse s pantngs and hs fakes done by dfferent forgers. We have transformed the raw data of the mages nto a wavelet bass, and then used specal sets of the coeffcents as the features talored by ANN towards separatng each of those classes. At frst, we have acqured orgnal and fake pantngs of Matsse as n Fgure 1. To acheve separaton, we have extracted essental features from pantngs usng Dscrete Wavelet Transform (DWT) [8, 9]. In wavelet approach, we have chosen (mean, mnmum, maxmum, varance, standart devaton) values of approxmaton, vertcal, horzontal and dagonal wavelet coeffcents of the whole pantngs. Then these 20 feature vectors were taken as nput of ANN structure. Thus, both compresson and unfcaton s acheved wth constant length of vector for varous pantngs wth dfferent szes. The overvew of our system s shown at Fgure MATERIAL We focus on orgnal and fake pantngs of Matsse and try to separate orgnal and fake pantngs objectvely and automatcally usng Wavelet and ANN approaches consecutvely. In ths secton, artststc works of Matsse and fakes of hm wll be gven, whch helps us to dentfy the problem. Matsse produced an enormous volume of work n fouvst styles. He created thousands of pantngs, prnts and sculptures durng hs lfe [7, 10-12]. There are also lots of fake of Matsse. He was especally faked by Elmyr de Hory who was a wellknown art forger of the 20 th century. He eventually became known worldwde as one of the most talented and greatest art forger. The Hungaran art forger clamed to have sold lots of fakes to galleres and museums around the world durng hs career. Hs nsstence that he had passed off pantngs by such artsts as Pcasso, Modglan and Matsse caused a scandal n the world of art and ts experts. We used some fakes of Matsse from de Hory for tranng and testng. Here, we have chosen 60 orgnal pantng mages from fouvst perod of Matsse and 15 fakes. 3. METHODS In ths secton, wavelet and ANN wll be explaned. The man mathematcal formulas of each wll be gven WAVELET BASED FEATURE EXTRACTION Wavelets decompose data nto dfferent frequency sub-bands components and then study each component wth a resoluton matched to ts scale. Wavelets have come out as powerful new mathematcal tools for analyss of complex datasets. In classcal approach, Fourer transform provdes representaton of an mage based only on ts frequency contents. Hence ths representaton s not spatally localzed whle wavelet functons are localzed n space. Whle Fourer transform groups a sgnal nto a spectrum of frequences whereas the wavelet analyss decomposes nto a herarchy of scales rangng from the coarset scale. Hence wavelet transform whch provdes representaton of an mage at varous resolutons s a better tool for feature extracton from mages [4, 13]. The wavelet transform s a useful mathematcal tool that currently has receved a great attenton n dfferent applcatons lke compresson and feature extracton. Feature extracton s defned that extracton some mportant features from the mage and obtanng feature vector. Wavelet transform s used as a feature extractor n ths study. Snce our raw data s dscrete, we use Dscrete Wavelet Transform whch employs a dscrete set of the wavelet scales and translaton obeyng some defned rules as an mplementaton of the wavelet transform. Here, both of scale and translaton parameters are dscrete. Thus, DWT can be represented n Eq.(1), m, n (1) x W[ m, n] f [ x] [ x]

3 793 Separaton of Orgnal Pantngs of Matsse And Hs Fakes usng Wavelet and Artfcal Neural where, dscretzed scale and translaton parameters are gven by, a 2 j ve b k2 j ( k, j Z ). Then, wavelet bass functon s wrten n Eq.(2), / 2, [ x j j j k ] 2 (2 x k ) (2) One-dmentonal transforms are easly extended to 2-dmensonal functons lke mages. In ths case, the DWT s appled to each dmenson separately. Ths yeld a multresoluton decomposton of the mage nto four subbands called the approxmaton (low frequency component) and detals (hgh frequency component). The approxmaton (A) ndcates a low resoluton of the orgnal mage. The detal coeffcents are horzontal (H), vertcal (V) and dagonal (D). Fgure 3 presents process of pantng mage beng decomposed nto approxmate and detaled components. At each decomposton level, the length of the decomposed mage s half the length of the sgnal n the prevous stage. The frst level decomposton of an N N mage s N / 2 N / 2 and the second level decomposton s N / 4 N / ARTIFICIAL NEURAL NETWORKS An Artfcal Neural Network (ANN) s an nformaton processng paradgm that s nspred by the way bologcal nervous systems, such as the bran, process nformaton. Artfcal Neural Network s also hghly parallel systems that process nformaton through many nterconnected neurons that respond to nputs through modfable weghts, thresholds and mathematcal transfer functons. Each unt processes the pattern of actvty t receves from other unts, than broadcasts ts response to stll other unts. An mportant applcaton of neural networks s pattern recognton. Pattern recognton can be mplemented by usng a feed-forward neural network that has been traned accordngly. Durng tranng, the network s traned to assocate outputs wth nput patterns [14]. A unt n the output layer determnes ts actvty by followng a two step procedure: Frst, t computes the total weghted nput x j, usng the formula: X y W (3) j j where y s the actvty level of the j th unt n the prevous layer and W j s the weght of the connecton between the th and the j th unt. Second, the unt calculates the actvty y j usng some functon of the total weghted nput. Typcally we use the sgmod functon: 1 (4) y j x j 1 e Once the actvtes of all output unts have been determned, the network computes the error E, whch s defned by the expresson: 1 E y d 2 ( 2 ) (5) Where s the actvty level of the j th unt n the top layer and d s the desred output of the j th unt. 4. EXPERIMENTAL RESULTS Wavelet coeffcents of 75 (60 orgnals, 15 fakes) mages are calculated. After decomposton wth Dscrete Wavelet Transform, we have four coeffcents for each mage; approxmaton, vertcal, horzontal and dagonal. Level-1 DAUB4 and HAAR type wavelet decompostons are preferred. The frst level decomposton vector sze s too large to be gven as an nput to a classfer. Snce hgh dmensonal of feature vectors ncreased computatonal complexty and hence, n order to reduce to dmensonalty of the extracted feature vectors, statstcs over the wavelet coeffcents are used. Then the followng statstcal features were chosen as follows; Maxmum values of the approxmaton, vertcal, horzontal and dagonal coeffcents Mnmum values of the approxmaton, vertcal, horzontal and dagonal coeffcents Mean values of the approxmaton, vertcal, horzontal and dagonal coeffcents Varance values of the approxmaton, vertcal, horzontal and dagonal coeffcents Standard devaton values of the approxmaton, vertcal, horzontal and dagonal coeffcents. The computed statstcal features of dscrete wavelet coeffcents are used as the nputs of the network of ANN. The output of the model was defned as 0 for orgnal pantngs of Matsse and 1 for fakes. In ths paper multlayer perceptron (MLP) ANN was used and we treat the separatons of pantng mages between orgnal and fakes as a two class pattern classfcaton problem. MLP-

4 794 Separaton of Orgnal Pantngs of Matsse And Hs Fakes usng Wavelet and Artfcal Neural ANN was traned and tested Backpropagaton learnng algorthms. For the tranng, the most sutable network confguraton found was 15 neurons n hdden layer. 5 statstcal features for each subband codng, totally 20 nputs for each pantng were used. On the other hand, ANN structure has 20 nput neurons, 15 hdden neurons and 1 output, n other presentaton t can be modeled as (20, 15,1). ANN structure s gven n Fgure 4. Durng the tranng procedure; we consdered 20 dfferent features of 50 pantngs as nput set. In testng procedure; we chose 20 dfferent features of the remanng 25 pantngs. We reached 88 % correct estmaton n testng stage wth HAAR type wavelet coeffcents. 5. CONCLUSION In ths study, we have classfed pantngs of orgnal Matsse and hs fakes wth the help of wavelet transform and ANN, whch are used for feature extracton and supervsed machne learnng approaches. We have preferred wavelet type s Level-1 Daub4 and Haar after expermental study. Snce, n the frst level decomposton, the vector sze s too large to be gven as an nput to ANN. So only some statstcs of frst level wavelet coeffcents are used as an nput of ANN. These are; {maxmum, mnmum, mean, varance and standard devaton values of the approxmaton, vertcal, horzontal and dagonal coeffcents}. Our expermentaton showed that classfcaton accuracy has reached up to 88 % by HAAR type Wavelet. Ths statstc suggests that our combned approach may be used to facltate separaton from orgnal pantng to ts fakes. REFERENCES 1. B. Gunsel, S. Sarel, O. Icoglu, Content-based access to art pantngs, Proc. of IEEE ICIP, Genova, Italy, September B. Temel, N. Klc N, B. Ozgultekn, O. N. Ucan, Separaton of Pcasso and Braque pantngs usng wavelet and artfcal neural networks, Internatonal Workshop III Mn Symposum on Applcatons of Wavelet to Real World Problems (IWW08), Istanbul, Turkey May L. Fausett, Fundemantels of neural networks archtectures, algorthm and applcatons, Englewood Clffs, NJ, USA, (1994). 4. S. Chaplot, L.M. Patnak, N.R. Jagannathan, Classfcaton of magnetc resonance bran mages usng wavelets as nput to support vector machne and neural network, Bomedcal Sgnal Processng and Control, 1 (2006) nuclear/cardat.html, Carbon Datng Lab1/Forgeres.pdf, Detectng Art Forgeres. 7. V. Essers, Matsse, Benedkt Taschen Press, Germany, I. Daubeches, Ten lectures on wavelets, CBMS Conference Lecture Notes 61. SIAM. Phladelpha M. Mst, Y. Mst, G. Oppenhem, J.M. Pogg, Wavelet Toolbox User s Gude, Mathworks nc, Massachusetts, N. Lynton, The Story of Modern Art, Phadon Press, England, S. Tansuğ, The Hstory of Pantng, Remz Press. Istanbul, Turkey, A. Turan, The Hstory of World Art, Remz Press. Istanbul, Turkey, R. C. Gonzales, R. E. Woods, Dgtal Image Processng, Addson Wesley, Readng, MA, N. Svr, N. Klc and O.N. Ucan, Estmaton of stream temperature n Frtna creek (Rze- Turkye) usng artfcal neural network model, J. Envron. Bol., 28 (2007), pp.67-72

5 795 Separaton of Orgnal Pantngs of Matsse And Hs Fakes usng Wavelet and Artfcal Neural FIGURES Henry Matsse, The Musc Lesson (La lecon de musque), 1917, Ol on canvas, 96 3/8 x 79 n. (244.7 x cm) Barnes Foundaton, Meron, PA Successon H. Matsse, Pars Elmyr de Hory, Fake of Matsse Fgure 1. Overvew of the smlarty Fgure 2. Overvew of the separaton system.

6 796 Separaton of Orgnal Pantngs of Matsse And Hs Fakes usng Wavelet and Artfcal Neural Fgure 3. One and Two Level Decomposton of Pantngs wth DWT Fgure 4. ANN Structure (20,15,1)