4International audienceIn this paper, we propose an evaluation of the impact of a codebook filtering step on the recognition rate of a Galois lattice classifier. Unlike the usual approach which only considers a whole visual dictionary and is likely to over-fitting, we boost the Galois lattice using a filtered dictionary by assigning a probability of appearance to each visual word in a symbol model. The retrieval performance and behavior of the method have been compared to state-of-the art and proved that is suitable to the recognition process. Experimental results show that the Galois Lattice classifier combined with a filtered codebook outperforms classic classifiers. Interestingly, due to the high selection of features from the dictionary, the accuracy improvement is obtained with a considerable computational cost reduction

Boumaiza, Ameni

Tabbone, Salvatore

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HAL Id: hal-00759516https://hal.archives-ouvertes.fr/hal-00759516Submitted on 30 Nov 2012HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Impact of a Codebook Filtering Step on a Galois LatticeStructure for Graphics RecognitionAmeni Boumaiza, Salvatore TabboneTo cite this version:Ameni Boumaiza, Salvatore Tabbone. Impact of a Codebook Filtering Step on a Galois LatticeStructure for Graphics Recognition. 21st International Conference on Pattern Recognition, Nov 2012,Tsukuba International Congress Center, Japan. pp.4. ￿hal-00759516￿Impact of a Codebook Filtering Step on a Galois Lattice Structure forGraphics RecognitionAmeni Boumaiza and Salvatore TabboneUniversité de Lorraine- LORIA UMR 7503, 54506 Vandœuvre-lès-Nancy, France{boumaiza, tabbone}@loria.frAbstractIn this paper, we propose an evaluation of the im-pact of a codebook filtering step on the recognition rateof a Galois lattice classifier. Unlike the usual approachwhich only considers a whole visual dictionary and islikely to over-fitting, we boost the Galois lattice usinga filtered dictionary by assigning a probability of ap-pearance to each visual word in a symbol model. Theretrieval performance and behavior of the method havebeen compared to state-of-the art and proved that issuitable to the recognition process. Experimental re-sults show that the Galois Lattice classifier combinedwith a filtered codebook outperforms classic classifiers.Interestingly, due to the high selection of features fromthe dictionary, the accuracy improvement is obtainedwith a considerable computational cost reduction.1 IntroductionWe address the task of graphics classification, whichaims to assign a category for a queried symbol. Moti-vated by the great success of the Galois lattice classifierin the text mining field [4], several techniques in thesymbol recognition field have been proposed [2, 5, 7, 1]which have shown very good performances. Thesestate-of-the-art methods require an intensive learn-ing/training stage due to the use of a big number of fea-tures either by the use of the bag of words [2] or bythe discretization step for features vectors [5]. We ar-gue that these two practices commonly used in symbolclassification methods, have led to good performancebut still also have some limits. For the purpose of bet-ter discrimination and less computational complexity,bag of words features representation should be reducedand filtered out which is indispensable and crucial to thedevelopment of a reliable graphics recognition system.The present work1 is part of an ongoing efforts [2, 3] toimprove the performance of a symbol recognition sys-tem based on the Galois lattice classifier by studyingthe impact of the codebook on the Galois lattice perfor-mances. We will see that the filtering of the codebookinto frequent visual words will boost the performanceof the lattice.2 Proposed approach2.1 Codebook GenerationIn the codebook generation, a clustering algorithm(e.g. k-means) takes the training image descriptors asan input and quantizes the descriptors into n clusters.Then, the center points of each cluster are used to definethe codebook and to give a visual word representationof features.2.2 Codebook FilteringWhen the size of the codebook is large and theamount of training data is small, empty or almost emptyclusters could be generated in the codebook. Thesesmall clusters might affect the performance of the sys-tem, and it might be possible to improve the perfor-mance by pruning empty and small bins out from thecodebook. This study aims to evaluate each part of thedictionary because rare visual words can be of majorinterest as frequent are. The smaller the pruning fac-tor, the fewer words in the codebook after pruning. Let|S| be the number of symbols in the database, |Ω| thesize of the dictionary, |Sk| the number of symbols in aclass k, |wik| the number of visual word i (= 1..Ω) de-scribing the symbols belonging to the class k. Then,Freq[wik, Sk] =|wik||Sk|is the frequency of appearance ofa visual word i into a class k. The higher the value of1This work was supported by the European project Eureka 4462SCANPLANFreq the more frequent is the visual word for the classi. For instance Freq = 1 means that the visual word iappears in each symbol of the class k and reciprocallyif Freq = 0 the class k is not encoded by the word ibut this word could encode another classes of symbols.2.3 Structure of the Galois Lattice ClassifierAlgorithm 1 Proposed Algorithm1. Codebook FilteringInput wi a visual word, Ω: the visual dictionary,S: the database, Freq[wi, Sk] the frequency ofappearance of the visual word i in the class kθ a threshold representing x% of the dictionary ofeach visual wordFor each wi ∈ Ω doIf Freq[wi. , S.]≥ θΩFreq=Push(wi.)else ΩNonFreq=Push(wi.)EndIf2. Building the Discretized Table of the Galois latticeIf a symbol S is characterized by wi thenthen R(S, wi.)=1else R(S, wi.)=0EndIf3. Building the Hass Diagram of the Galois latticebased on the Filtered Codebook (For more details,we use a modified Bordat algorithm for the buildingstep[6])For k=1 to NumberOfSymbolsPerCategoriesFor i = 1 to NumberOfSymbolsPerCategory C[k]For j = 1 to NumberOfConceptsif ((xi, yi, FIND (xi, yi)=FALSE)For j=1 to NumberOfAttributesInsert(A{VisualWords[j]}) in the Galois lat-tice=f(C{VisualWords[i]}))OCk(xi, yi)=C{k}(xi, yi)else ”Concept already exists in the Galois lattice”EndforEndforEndforOutput The Galois Lattice:{Symbols, FilteredProp-erties}Our approach aims to find the frequent word ofthe dictionary Ω which guarantees the best recogni-tion rate of the Galois Lattice following the frequentand non-frequent sets of words defined respectivelyas: ΩFreq: wik →R, where Freq[wi, Sk]≥ θ, andΩNonFreq =ΩFreq .The Galois lattice (GL) based only on the frequentvisual words is named in this paper the Dominant Ga-lois Lattice (DGL) and NDGL (Non Dominant GaloisLattice) for the non-frequent words (see Algorithm 1).2.4 The traversal of the Hass diagram of theGalois lattice for the classification phaseGiven a query symbol, and its visual words, the Hassdiagram is traversed in order to recognize the querysymbol. A traversal of the Hass diagram often leadsto visit all the concepts containing a set of attributes de-scribing the queried symbol. The node containing theprimitives of the query is returned as an result of therecognition process.3 Experimental ResultsIn order to carry out our experiments we use theGREC2003 symbol database as models. By runningexperiments with different sizes of the visual dictio-nary, we study the behavior of the lattice and more pre-cisely how it would perform either with frequent or non-frequent words as shown in Figure 1.3.1 Impact of the Codebook size on the recog-nition rate of the Galois Lattice classifierIn this section, we will study the impact of the visualdictionary filtering step on the Galois lattice recogni-tion performances. This evaluation provides an empir-ical basis for designing visual-words that are likely toproduce superior classification performance. In our ap-proach, we opt to a specific criterion for measuring the”informativeness” of each word in order to eliminate theleast informative ones before the classifier constructionstep. As shown in Figure 1, the size of the codebook iscrucial for the performance. It affects features that aregenerated and fed to the Galois lattice classifier. Whenthe codebook size is less than 100 visual words, we re-mark in Figure 1. a and b that the system looses its abil-ity to classify queried symbols when using frequent ornon frequent features separately. This can be explainedby the fact that with fewer words in the codebook, fea-tures are not distinctive enough, and when using thewhole vocabulary Ω the system can recognize symbolsbetter. We conclude that if the codebook is pruned toomuch then it looses important characteristics especiallywhen its size is smaller. When the size of the vocabulary‖Ω‖ is larger than 300, DGL based on dominant visualwords ({Ω}\{ΩNonFreq)} shows best results becausethe most rare words in their corresponding symbol classare removed in the pruning step. This technique im-proves the classifier performances and when the dictio-nary is pruned, it performs better than the GL which isnot pruned. We remark also that when ‖Ω‖ varies from500 to 800, the performance of the DGL still stable andconstant. The NDGL gives bad results in the classifica-tion step and many outliers appear when returning con-cepts from the Galois lattice classifier which are likelyto contain the queried symbol.3.2 Impact of the probability criterion θ onthe Galois Lattice recognition rateThe experiment shown in Figure 2 suggests to searchfor the optimal value of the parameter θ to be used as athreshold to prune the dictionary and build a compet-itive classifier. We examine the optimal parameter onthe best vocabulary size. We found that if the probabil-ity of appearance of a word is greater than θ, the recog-nition rate of the Dominant Galois lattice is stable andsubstantially improved when using the frequent wordsof the vocabulary. The Frequent words extracted fromthe visual dictionary and which have the most impor-tant probability of appearance in the symbol databaseare clearly more effective since they are used by our ap-proach based on the Dominant Galois lattice. We canremark that following the size of the codebook, we gotthe best results when we remove at least half (θ ≃ 0.5)of the visual words less frequent in each class. Whenthe size of the visual vocabulary is small (50) the nonfrequent words are highly correlated to the frequentwords and participate on the good recognition rate re-turned by the classifier. The rare words are informa-tive enough and are complementary to frequent visualwords that ’s why it will be better to keep the wholecodebook to give satisfiable information about a sym-bol category. When the size of the codebook is dou-bled from 100 to 200, the performance of the systemimproves with pruned codebooks. However, the differ-ence in the performances between a non-pruned code-book and the best pruned codebook is minor. Althoughthe difference between non-pruned and the best prunedcodebook is relatively small, it is significant especiallywhen the size of the codebook is 200 or larger. Indeed,with the 500 words codebook, the performance is highwhen the codebook is pruned with a factor greater than0.5 and the best recognition rate is obtained when wekeep only the representative visual words (i.e. θ = 1means that Freq=1) in each class. We remark that thereis a large gap between the performance of the Galoislattice based on frequent attributes and the Galois lat-tice based on the non frequent attributes which can beexplained by the fact that frequent words are widely-spread in the symbol database, and therefore they bringa sufficient information to classify a queried symbol us-ing our classifier. In this case, we can remove all thenon frequent words without loss of symbol classifica-tion accuracy. These words are not very relevant to thecategory of the graphical symbol. As shown in this ex-periment, the recognition rate rises significantly whenusing frequent words ΩFreq for building the classifier,whereas, when using the non frequent words ΩNonFreqthe level drops in comparison with ΩFreq .This experiment shows that when the size of thecodebook increases the symbol are better described butwe add noise in the representation. In this case we im-prove the performance of the classifier if we keep themore informative words in each class. On the con-trary, if the size of the codebook is small we reducethe noise, all the words are useful but the descriptionis poor. Therefore, the performance of the classifier islower.Figure 2. Comparison between the recog-nition rate of the DGLΩFreq , NDGLΩNonFreqand GLΩ as a function of θ. In this experi-ment the size of the vocabulary ‖Ω‖ is setbetween 50 and 500 visual words.3.3 Comparison with the State of the ArtIn Table 1 (column 2) we show the obtained recog-nition rates when considering just the topmost visualwords of the vocabulary, in (column 3) we show theperformances of the NDGL based on rare visual words.This experiment indicates that the performance is alsohighly dependent on the filtering step of the codebook.With our approach which is based on the highest level offrequency of appearance (dominance criterion) reachedby visual words, the classifier attains good recognitionrates whereas in the lowest level of frequency of appear-ance some symbol designs are badly recognized pro-voking some outliers. For instance, as shown in (row4,column2) and (row4, column3), we can remark that therecognition rate obtained using the DGL (98.4%) is sig-[a] [b] [c][d] [e] [f]Figure 1. Comparison between the performances of the three classifiers: DGLΩFreq ,NDGLΩNonFreq and the GLΩ as a function of the size of the dictionary (a) ‖Ω‖=50, (b): ‖Ω‖=100,(c): ‖Ω‖=300, (d): ‖Ω‖=400, (e): ‖Ω‖=500, (f): ‖Ω‖=800.nificantly greater than the other methods based on theGalois lattice too. In addition, our approach guaran-tees a reduced processing time during the traversal ofthe Hass diagram (109.7s for our approach instead of231.74s for [5] and 153.85s for the Galois lattice basedon the whole dictionary. The time needed during theclassification step is lower because the number of nodesis reduced in the lattice. Due to the high features se-lection level to build the Galois lattice, there is a largernumber of un-informative concepts or nodes eliminatedfrom the graph. The concept which is likely to con-tain the category of the queried symbol is returned ina reduced time in comparison with the classical Galoislattice. Therefore, the number of concepts is reducedwhen using our approach (2100 nodes) instead of 3170nodes for the GL and 4230 nodes for [5].Table 1. Performance evaluation of GL[2],DGL, NDGL and GL[5] in term of NC: Num-ber of Concepts, PT: Processing Time andRR: Recognition Rate.Approach GL[2] DGL NDGL Approach in [5]NC 3170 2100 1050 4230PT(s) 153.85 109.7 67.45 231.74RR(%) 96.08 98.4 43.02 94.624 ConclusionsIn this paper, we study the impact of the codebookfiltering step on classifier performance. From the ex-periments, we can remark that keeping only dominantwords improve the performance of the lattice in terms ofrecognition rate and time complexity. More precisely,when the size of the codebook increases we improvethe description of symbols but we add noise in the rep-resentation. In this case we improve the performance ofthe classifier if we keep the more informative words.References[1] S. Barrat and S. Tabbone. A bayesian network for com-bining descriptors: application to symbol recognition. In-ternational Journal on Document Analysis and Recogni-tion, 13(1):65–75, 2010.[2] A. Boumaiza and S. Tabbone. A novel approach forgraphics recognition based on galois lattice and bag ofwords representation. In ICDAR, pages 829–833, 2011.[3] A. Boumaiza and S. Tabbone. Symbol recognition usinga galois lattice of frequent graphical patterns. In 10thIAPR Workshop on Document Analysis Systems (DAS),2012.[4] C. Carpineto and G. Romano. Using concept lattices fortext retrieval and mining. In Formal Concept Analysis,pages 161–179, 2005.[5] M. Coustaty, S. Guillas, M. Visani, K. Bertet, and J.-M.Ogier. On the joint use of a structural signature and agalois lattice classifier for symbol recognition. In GREC,pages 61–70, 2007.[6] B. Ganter and R. Wille. Formal concept analysis - math-ematical foundations. Springer, 1999.[7] T.-O. Nguyen, S. Tabbone, and O. R. Terrades. Sym-bol descriptor based on shape context and vector modelof information retrieval. In Document Analysis Systems,pages 191–197, 2008.

Impact of a Codebook Filtering Step on a Galois Lattice Structure for Graphics Recognition

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Impact of a Codebook Filtering Step on a Galois Lattice Structure for Graphics Recognition

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