Majlesi Journal of Electrical Engineering Vol. 4, No. 4, December 2010

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1 A Herarchcal Classfcaton Structure based on Tranable Bayesan Classfer for Logo Detecton and Recognton Hossen Pourghassem Young Research Club-Islamc Azad Unversty- Najafabad Branch, Iran. Emal: Receved: March 1 Revsed: June 1 Accepted: July 1 ABSTRACT: The ever-ncreasng number of logo (trademark) n offcal automaton systems for nformaton management, archvng and retreval applcatons has created greater demand for an automatc detecton and recognton logo. In ths paper, a herarchcal classfcaton structure based on Bayesan classfer s proposed to logo detecton and recognton. In ths herarchcal structure, usng two measures false accept rate (FAR) and false reject rate (FRR), a novel and straghtforward tranng scheme s presented to extract optmum parameters of the traned Bayesan classfer. In each level of the herarchcal structure, a separable feature set of shape and texture features s used to tran and test classfer based on complexty of the logo pattern. The logo canddate regons are extracted from document mages by a wavelet-based segmentaton algorthm, and then recognzed n the proposed structure. The proposed structure s evaluated on a vast database consstng of the document and non-document mages wth Persan and nternatonal logos. The obtaned results show effcency of the proposed structure n the real and operatonal condtons. KEYWORDS: Logo detecton and recognton, tranable Bayesan classfer, tranng scheme, shape and texture features and wavelet-based segmentaton algorthm. 1. INTRODUCTION Due to the ncreasng content of multmeda databases (such as text, mage, audo, and vdeo), the demands for archvng and retrevng n search and data mnng applcatons s a vtal requrement. In ths doman, document mage analyss and understandng have receved a great deal of nterests n the last few years for many dverse applcatons such as, dgtal lbrary, Internet publshng and searchng, on-lne shoppng and offcal automaton systems. Along wth logo detecton and recognton s an mportant requrement n the document mage analyss and shape-matchng doman as t enables us to dentfy the source of documents based on the organzaton where a document orgnates. For example, n offcal automaton systems applcaton, content of scanned offcal letters (as a document mage) should be recognzed and classfed by ther logos. Therefore, logos can act as a valuable means n dentfyng sources of documents [1]. The major prevously research related to logo n document mages have focused n logo recognton []- [8] and rare nvestgaton have conssted of both logo detecton and recognton [9], [1]. In [11], a modfed lne segment Hausdorff dstance has proposed that ncorporates structural and spatal nformaton to compute dssmlarty between two sets of lne segments rather than two sets of ponts. In ths paper, logo s frst generated to lne segments and represented wth feature vector. In [9], a logo detecton system s presented based on segmentaton the document mage nto smaller mages usng a top-down X-Y cut algorthm [1]. In ths paper, sxteen features of the connected components n each segment are extracted and used by a rule-based classfcaton scheme. In [13], an approach to logo detecton and extracton n the document mages usng a mult-scale boostng strategy has presented. An ntal two-class Fsher classfer at a coarse mage scale on each connected component s used. Each detected logo canddate regon s then classfed at fner mage scales by a cascade of smple classfers [13]. In [1] a smple logo detecton method has presented based on the assumpton that the spatal densty of foreground pxels n a logo regon s greater than that n non-logo regons. A document mage s frst bnarzed nto foreground and background pxels. Then, the spatal densty wthn each fxed sze wndow s computed and the regon wth the hghest densty s hypotheszed as a logo regon. In [14], a method for such a system based on the mage content, usng a 16

2 shape feature has presented. Zernke moments of an mage are used as a feature set. In ths method, to recognze the detected logo, a smlarly measure based on shape feature (Zernke moments) has defned. In [], an automatc content-based logo retreval method has proposed. The proposed method n [] automatcally selects approprate features (such as area, devaton, symmetry, centralzaton, complexty and - level contour representaton strngs) based on feature selecton prncples to dscrmnate logo. In ths method, the user can submt a query through logo examples to get a lst of database trademarks ordered by smlarty ranks. In [4], a color mage retreval system based on multple classfers has presented. In ths approach, a regon-growng technque for segmentaton of the nput mage nto logo canddate regons and three complementary regon-based classfers (color, shape and relatonal classfers) have appled to logo recognton. In each classfer, a vrtue probablty representng the probablty that an mage s smlar to the query mage s defned. A set of vrtue probabltes s calculated to defne smlarty measure n each classfer. In [16], a shape-based smlarty retreval system has developed based on database classfcaton, whch explots the contour and nteror regon of a shape effcently. In ths paper, angular radal transform (ART) regon feature s employed to compare the query wth the canddate sets accordng to the prorty order. In ths paper, a herarchcal classfcaton structure based on Bayesan classfer for logo detecton and recognton s proposed. In ths structure, a novel tranng approach s used to extract the optmum parameters of Bayesan classfer by false accept rate and false reject rate measures. In each level of ths structure, an solated set of shape features consstng of Seden features [9], Yn features [], ART features [17], and texture features consstng of energy, homogenety, correlaton extracted from co-occurrence matrx [18] are extracted based on logo complexty. The used features n dfferent decson levels; from down levels to up levels are more ntrcate. In other word, smple and ntrcate features are used to smple and ntrcate logos, respectvely. The proposed structure n ths paper has two dfferences wth the prevously works. Frstly, as we prevously mply that the major prevously research related to logo n the document mages have focused n logo recognton and rare nvestgaton have conssted of both logo detecton and recognton. Whle, n ths paper, these two stages have merged n a prmtve segmentaton stage and a logo canddate regon classfcaton stage. Secondly, the used mages n the prevously research are scanned offcal letters. Whle, the used database for evaluaton of the proposed algorthm conssts of a large number of mages such as personal, journal and newspaper mages and a few document mages. Certanly, evaluaton of the proposed algorthm wth ths database not only obtans real condtons of tests but also asks more accuracy n algorthm desgn. Ths paper s organzed as follows: Secton descrbes the used segmentaton algorthm. In secton 3, herarchcal structure of logo detecton and recognton s presented. In ths secton, detals of ths structure consstng of the extracted features n each level, tranng approach of classfers and logo classfcaton and recognton procedure are descrbed. Expermental results are shown n Secton 4. Secton 5 provdes a concluson to the work.. SEGMENTATION The frst stage n logo detecton and recognton procedure s the detecton and extracton of canddate logo regon from document mages. The prevous used approaches are dvded to two general categores. Approaches of the frst category use morphologcal operators for logo detecton purpose [19]. Fg. 1. Block dagram of wavelet-based segmentaton algorthm [1]. Fg.. Block dagram of herarchcal classfcaton structure of the logo canddate regon. 17

3 Approaches of the second category defne and use measures such as spatal densty of the foreground pxel n the mage, relatve and spatal nformaton of the objects n the mage and color features of logo [4], [1], [13], []- []. In ths paper, a segmentaton algorthm of the document mage n page layout analyss applcaton s used [1]. In [1], a two-stage segmentaton algorthm based on wavelet transform and thresholdng has proposed. Fgure 1 shows block dagram of the wavelet-based segmentaton algorthm. In the waveletbased segmentaton algorthm, segmentaton s carred out on the wavelet transform subbands of the grayscale document mage. Seven sets of the wavelet transform subbands (approxmaton and detals n three drectons, vertcal, horzontal and dagonal) are formed n two levels of the wavelet transform. Small coeffcents of subbands are replaced wth zero value by applyng a threshold value as a de-nosng processng step. Threshold value of de-nosng process s dependent to energy of each subband. It s determned by below equaton: Th Mn( coeff ) ( Max( coeff ) Mn( coeff )) (1) where Mn (coeff ) and Max (coeff ) are the mnmum and maxmum values of the wavelet transform coeffcents n each subband, respectvely. s a constant that we set to.5. Neghbor pxels n the wavelet subbands are amplfed by dlaton morphologcal operator, untl segmented regons of the document mage are formed. The detected segments by the wavelet-based segmentaton algorthm are removed from the document mage and the rest of the mage s segmented by the threshold-based segmentaton algorthm. In ths stage, non-segmented regons are dvded to foreground and background segments by applyng a threshold that s determned from gray-level hstogram based on Otsu s method [3]. Then foreground segments are labeled and extracted from mage as text or pcture segments. 3. HIERARCHICAL STRUCTURE OF LOGO DETECTION AND RECOGNITION In ths secton, logo detecton and recognton procedure s descrbed based on the canddate logo regons. The decson-makng procedure of the logo canddate regons n pre-defned logo classes s performed n a herarchcal structure. Fgure shows block dagram of the herarchcal classfcaton structure of logo canddate regon. In ths structure, decson s carred out n the three level or three classfers. It does not mean that the three classfers classfy each nput regon. However, n the worse case, the tertary classfer determnes the type of the nput regon. In other word, more complcated pattern of the logo causes more complex feature set and tranng stage. Detals of the herarchcal structure consst of Bayesan classfer; the used features n each classfer and tranng procedure of the classfers are descrbed n the followng Bayesan Classfer (Mnmum Dstance) In the proposed structure, Bayesan classfer (Mnmum dstance) s used. A Bayesan classfer wth c dscrmnate functons corresponds to c classes and p x w ) ~ N(, ), I are defned as ( x g x P c () ( ) ln ( ) 1,,.., t where x ( x ) ( x ).If pror probabltes ( P w ) are the same for all c classes. Then the ln P ( w ) term becomes unmportant addtve constant that can be gnored. Therefore, Bayesan dscrmnaton functon smplfes as below, g ( x) x 1,,.., c (3) The above equaton s called a mnmum dstance classfer. 3.. Feature Extracton In ths paper, three dfferent feature sets corresponds to three levels of the herarchcal logo detecton and recognton structure are used. The frst feature set conssts of Seden features [9] and Yn features such as area, devaton, symmetry, centralzaton and complexty []. The second feature set conssts of energy, homogenety, correlaton extracted from cooccurrence matrx [18] and the tertary feature set conssts of ART features [17]. In the followng, ART feature s descrbed n detals Angular Radal Transform features ART s the -D complex transform defned on a unt dsk that conssts of the complete orthonormal snusodal bass functons n polar coordnates. The transformaton s defned as [17] Fnm Vnm(, ), f (, ) 1 (4) * V (, ), f (, ) d d nm Here, F s an ART coeffcent of order n and m, nm f (, ) s an mage functon n polar coordnates, and V nm(, ) s the ART bass functon that are separable along the angular and radal drectons,.e., Vnm(, ) Am ( ) Rn ( ) (5) The angular and radal bass functons are defned as follows: 1 A m ( ) exp( jm ) (6) 1 n R ( n ) cos( n ) n (7) 18

4 To descrbe a shape, all pxels consttutng the shape are transformed wth ART, and the transformed coeffcents are formed nto the ART descrptor. Twelve angular and three radal functons are used. By dscardng the DC coeffcent, 35 AC components form the descrptor vector Tranng of Classfers In ths paper, three Bayesan classfers n the three levels of the herarchcal structure determne class type of unknown logo regon whle false accept and false reject rates of the classfers are less than two determned thresholds. Block dagram of the tranng procedure of the herarchcal classfers s shown n Fgure 3. In ths block dagram, usng tran and test sets and segmented regons, optmum parameters of the classfers are determned. In the followng, detals of ths block dagram are descrbed Tran and Test Samples To determne the optmum parameters of the classfers n the herarchcal structure, three sets of data are used. The frst data set s the segmented regons from document mages by segmentaton algorthm that s called database of detected regons n the block dagram of Fgure 3. The other two sets are tran and test samples of desrable logos of user. Tran and test samples are created of the scaled and rotated versons of the desred logo Tran set conssts of the orgnal logo, rotated samples wth rotaton angels of 7 and reszed samples wth scale coeffcents of.7 and 1.. Test set conssts of fve reszed samples wth scale coeffcents of.65,.85,.95, 1., 1.35 and 6 rotated samples wth rotaton angles of 3, 6 and 9 and 4 rotated and reszed samples wth rotaton angles and scale coeffcents of, 5 and.8,.9, 1. and 1.3, respectvely. Ultmately, test samples and 5 test samples are formed Determnaton of optmum classfcaton parameters Feature vector of an unknown logo x s belong to class f prototype of class ( ) s the nearest prototype n all prototypes to sample x based on equaton (3). But, we use ths classfer n another applcaton,.e. to am to false reject rate less than threshold, n each classfer of herarchcal structure. It may that an nput logo belongs to many predefned classes of logos. It causes that false accept rate ncreases. Now f false accept rate of the frst classfer s more than threshold, and then the second classfer classfes the nput logo by features that are more ntrcate. Now f false accept rate of the second classfer s not less than threshold. Ultmately, the tertary classfer carres out the fnal classfcaton. In each classfer, the maxmum value of dscrmnate functon g (x) for acceptance of the logos n the class s saved as optmum parameters of the tranng stage when false accept and false reject rates are less than thresholds and ( FAR % and FRR % ), respectvely. Fg.3. Block dagram of the tranng procedure of the herarchcal classfers. 19

5 Fgure 4 shows graph of false accept rate versus false reject rate and desred and undesred regons for a classfer. In ths graph, performance of classfer s accept that fall nto regons wth FAR % and FRR %. Whereas, the other regons of graph wth FRR % and FAR % are not accept that they are called undesred regons. In these cases, other classfers wth more complcated features are used unavodably. logos, a secton of text regons and a secton of pure pcture. Fg. 4. Graph of false accept rate versus false reject rate and desred and undesred regons for a classfer. 4. RESULTS The used document mage database s a collecton of 16 mages comprsng three classes, document mage (mages only wth text regons), pure pcture (mages wthout text regons) and combned mages (mages wth text and pcture regons). Ths database s used to evaluate the two-stage segmentaton algorthm. The used logo database s a collecton of 198 mages comprsng nternatonal and Persan logos. To evaluate the proposed logo detecton and recognton algorthm, these logos have nserted to document mages manually. Image sample of the document mage database are shown n Fgure (5). In the followng, results of the wavelet-based segmentaton algorthm and tranng procedure of Bayesan classfer are descrbed. Fg.5. Image sample of document mages database. Fg.6. Image sample of the extracted regons by the wavelet-based segmentaton algorthm Wavelet-based Segmentaton Algorthm In ths paper, the wavelet-based segmentaton algorthm s appled to detect the logo canddate regons. The wavelet-based segmentaton algorthm uses morphologcal operators n the wavelet subbands to detect text and shape regons [1]. Image sample of the segmented regons by the wavelet-based segmentaton algorthm s shown n Fgure (6). In Fgure (6), the segmented regons consst of shape Fg. 7. Image sample of desred logos for classfcaton problem (from left to rght correspondng to class 1 through 5, respectvely).

6 4.. Extracton of optmum classfcaton parameters Predefned classes of the extracted regons by the wavelet-based segmentaton algorthm are determned usng herarchcal structure. In ths herarchcal structure, usng three Bayesan classfers and test and tran samples, the optmum parameters of tranng such as mean value of each class as prototype sample and the maxmum value of g (x) for acceptance of samples are calculated. As you can see n Fgure 4, thresholds and parameters of classfers are desgned that the rates of false accept and false reject are less than two thresholds and, respectvely. In each level of herarchcal classfcaton, f false accept rate s more than whle false reject rate less than, then class of the unknown regon wll determne n the next level. Ths strategy s desgned that the rejected logos are decreased whle false accept rate of rrelevant logos be n an acceptable lmt. samples of the test samples have very large varance and accumulaton of all samples n a class s rratonal (notce that rotated and scaled logos wth large values form new logos.). Therefore, t s accepted, f 6 samples of the test samples reject (.e. 4% ). Threshold of false accept rate s set less than 1% (.e. 1% ). Table 1 shows classfcaton results of the logo canddate regon wth shown logos n Fgure 7. The selected logos n ths test have complex pattern and consderable smlarty wth other logos. These problems cause acceptance of 3, 11 and 5 logos of rrelevant logos n the frst, second and ffth classes, respectvely. It s happened whle the total of logo canddate regon correctly accepted. In other word, accuracy rate of logo detecton s 1%. Of course, a number of regons are detected ncorrectly as logo canddate regon. Fgure 8 shows mage sample of false accept cases of classfcaton problem correspondng to logos of Fgure 7. False accepted logos n Fgure 8 are smlar to orgnal logo n each class. Therefore, error of classfcaton s justfable. In Table 1, false rejected logos n each class are reszed and rescaled versons of orgnal logo wth very large rotaton angles and scale coeffcents. Therefore, the rejecton of these logos s admssble. Ultmately, accuracy rate of the proposed algorthm s 97.6% for a 5-class classfcaton problem. 5. CONCLUSION AND COMPARISON In ths paper, a herarchcal classfcaton structure based on tranable Bayesan classfer for logo detecton and recognton s proposed. In ths structure, a novel tranng approach s used to extract the optmum parameters of Bayesan classfer such as mean of samples and the maxmum value of dscrmnate functon for acceptance of a sample n a class by false accept and false reject rates. In each level of ths structure, an solated set of shape and texture features are extracted based on logo complexty. The used features n dfferent decson levels; from down levels to up levels are more ntrcate. The proposed structure has two man advantages, logo detecton and recognton stages usng shape and morphologcal features smultaneously and generalzaton of tranng usng the proposed tranng procedure of Bayesan classfer. (a) (b) (c) (d) Fg. 8. Image sample of false accept cases of classfcaton problem correspondng to logos of Fgure 7. (a) Class 1, (b) Class, (c) Class 5 and (d) Class rejecton. The proposed structure evaluated on a very large database consstng of the document and non-document mages wth Persan and nternatonal logos. Tests have 1

7 carred out n the worse case condtons. False accept and false reject rates of less than 1% and 4% have obtaned respectvely for a 5-class classfcaton problem. These results are very good n the worse case of test condtons. The segmentaton algorthm has obtaned false reject rate of zero for logo canddate regon detecton stage. An exact comparson across the presented algorthms n the lterature s a complex task (especally as no standard dataset s avalable at ths tme). However, for example n [], a retreval logo structure has presented n Chna logo applcaton. Recall rate of 76% has reported for retreval of the frst ten relevant logos. In [], a shape feature set and relevance feedback mechansm are used to mprove the performance. 6. ACKNOWLEDGMENT Ths work was supported by Young Research Club- Islamc Azad Unversty Najafabad Branch (IAUN) based on approved research proposal. Class Reject class Total Table 1. Classfcaton Results of logo canddate regons. The number of logos True Accept False Accept False Reject False Accept Rate (%) REFERENCES [1] H. Pourghassem, Page Layout Analyss of the Document Image based on Regon Classfcaton n a Herarchcal Decson Structure, Journal of Electroncs and Power Engneerng, Vol., pp , Sprng, (9) [] M. Gora, M. Maggna, S. Marnab, J.Q. Shengc, G. Sodab, Edge-backpropagaton for nosy logo recognton, Pattern Recognton, Vol. 36, pp , (3) [3] D. Doermann, E. Rvln, and I. Wess, Applyng algebrac and dfferental nvarants for logo recognton, Machne Vson and Applcaton, Vol. 9, No., pp.73-86, (1996) [4] I. S. Hseh, K. C. Fan, Multple Classfers for Color Flag and Trademark Image Retreval, IEEE Transactons on Image Processng, Vol. 1, No. 6, pp , (1) [5] J. Neumann, H. Samet, and A. Soffer, Integraton of local and global shape analyss for logo classfcaton, Pattern Recognton Letters, Vol. 3, No. 1, pp , () [6] P. Suda, C. Brdoux, B. Kammerer, and G. Maderlechner, Logo and word matchng usng a general approach to sgnal regstraton, In Proc. Int. Conf. Document Analyss and Recognton, pp , (1997) [7] K. Zyga, J. Schroeder, and R. Prce. Logo recognton usng retnal codng. In Proc. 38 th Aslomar Conf. Sgnals, Systems and Computers, Vol., pp , (4) [8] E. Francescon, P. Frascon, M. Gor, S. Marna, J. Q. Sheng, G. Soda, A. Sperdut, Logo Recognton by Recursve Neural Networks, Graphcs Recognton Algorthms and Systems, Vol. 1389, pp , (1998) [9] S. Seden, M. Dllencourt, S. Iran, R. Borrey, T. Murphy, Logo detecton n document mages, Proc. of the Int. Conf. on Imagng Scence, Systems, and Technology, Las Vegas, Nevada, pp , (1997) [1] T. Pham, Unconstraned logo detecton n document mages, Pattern Recognton, Vol. 36, No. 1, pp , (3) [11] Jngyng Chen, Maylor K. Leung,Yong sheng Gao, Nosy logo recognton usng lne segment Hausdorff dstance, Pattern recognton, Vol. 36, pp , (3) [1] G. Nagy, S. Seth, and M. Vswanathan, A prototype document mage analyss system for techncal journals, Computer, Vol. 7, pp. 1-, (199) [13] G. Zhu, D. Doermann, Automatc Document Logo Detecton, 9 th Int. Conf. on Document Analyss and Recognton, Vol., pp , (7) [14] Y.S. Km, W.Y. Km, Content-based trademark retreval system usng a vsually salent feature, Image and Vson Computng, Vol. 16, pp , (1998) [] P. Y. Yn, C. C. Yeh, Content-based retreval from trademark databases, Pattern Recognton Letters, Vol. 3, pp , () [16] M. H. Hung, C. H. Hseh, C. M. Kuo, Smlarty retreval of shape mages based on database classfcaton, J. Vs. Communcaton and Image Representaton, Vol. 17, pp , (6) [17] W. Y. Km, Y. S. Km, A new regon-based shape descrptor, ISO/IEC MPEG99/M547, TR-1, Mau, Hawa, (1999) [18] R. M. Haralck, K. Shanmugan, I. Dnsten, Textural features for mage classfcaton, IEEE Trans on Sys Man and Cybern, Vol. 3, No. 6, pp , (1973) [19] W.Q. Yan, J. Wang, M.S. Kankanhall, Automatc vdeo logo detecton and removal, Multmeda Systems, Vol. 1, No. 5, pp , (5) [] H. Wang, Y. Chen, Logo Detecton n Document Images Based on Boundary Extenson of Feature Rectangles, 1 th Conf. of Document Analyss and Recognton, pp , (9) [1] K. Gao, Sh. Ln, Y. Zhang, Sh. Tang; D. Zhang, Logo detecton based on spatal-spectral salency and

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