Linear combination of multiresolution descriptors : application to graphics recognition /

Consultable des del TDXEn el camp de l'Anàlisi de Documents voldríem ser capaços de processar automàticament qualsevol tipus de document digital i d'extreure la informació rellevant. és a dir, voldríem conËixer la configuració del document, identificar cadascuna de les seves parts i reconËixer els seus continguts; per a poder fer cerques entre les components del document, però també, per fer cerques entre documents diferents. Aquest és un problema difícil que ha motivat diferents línies de recerca a diferents nivells. S'ha desenvolupat tot una sèrie de tècniques destinades a pre-processar la imatge per augmentar la seva qualitat, reduint el soroll dels sistemes d'adquisició i minimitzant els efectes de la degradació dels documents. També trobem molts treballs en la segmentació destinats a separar les àrees d'interès de la resta del document. Finalment, des de finals dels anys 60 fins a l'actualitat s'han proposat molts tipus descriptors que pretenen representar i identificar aquestes àrees d'interès. En aquesta tesis ens hem centrat en el darrer d'aquests problemes, la descripció de formes però també en la fusió de classificadors per a aplicar-los a una de les apliacions de l'Anàlisi de Documents, el reconeixement de símbols gràfics. En el reconeixement de formes, moltes aplicacions han de fer front al problema de descriure un conjunt gran i complex de formes per a reconèixer-les, o per a recuperar-les de gran bases de dades. En alguns casos, a més del gran nombre de formes, podem trobar altres dificultats com són la semblança entre formes o la variabilitat de classes de símbols. En aquest casos, un punt clau en el procés de reconeixement de formes és la definició de descriptors de gran capacitat de discriminació. Malauradament, un sol tipus de descriptors no sol ser suficient per aconseguir resultats satisfactoris i per tant, hem de combinar la informació provinent de diferents fonts per a millorar el comportament global del sistema de reconeixement. Aquesta combinació de la informació la hem realitzat a travÈs de la fusió de classificadors. En relació a la descripció de formes, tradicionalment els símbols gràfics s'han representat mitjançant descriptors estructurals, construïts a partir d'una representació vectorial. Els mètodes de vectorització són sensibles al soroll i a les distorsions dels símbols esboçats. Podem intentar evitar aquest problema definint gramàtiques o construint models deformables dels símbols. Una altra possibilitat, la que hem seguit en aquest treball, és fer servir descriptors que no necessiten d'una representació vectorial. En el context de la descripció de formes hem proposat un descriptor basat en la transformada de crestetes -en anglès «ridgelets»- que, gràcies a que hem unificat la terminologia i hem introduït un vocabulari per explicar i classificar els descriptors, podem definir com: multiresolució, polar, 2D, que conserva la informació i invariant a les similituds. D'altre banda, la propietat de multiresolució de la transformada de crestetes fa que obtinguem una representació en diferents nivells de resolució que ens permet dividir-la en grups de coeficients de crestetes que es poden considerar com a descriptors. D'aquesta manera, hem entrenat un classificador per a cada descriptor, i hem proposat unes regles de combinació lineals, IN i DN, que minimitzen l'error de classificació per aquells classificadors que compleixin un conjunt de restriccions, relatives a la distribució i dependËncia dels classificadors. Aquests enfocs teòrics han estat avaluats a partir d'un conjunt d'experiments que ens han donat els següents resultats: Els descriptors de crestetes descriuen millor els símbols que altres descriptors més genèrics. Els mètodes IN i DN redueixen l'error de classificació en relació a d'altres mètodes de referència. Per últim, el mètode IN aplicat als descriptors de crestetes, en combinació amb classificadors de tipus «boosting» aconsegueix uns encerts de reconeixement propers als 100% en les proves definides per a la base de dades de símbols gràfics del GREC'03.In the field of Document Analysis we would like to be able to automatically process any kind of digital document. We mean extracting the document layout and identifying each of its parts, recognising its contents and organising them in order to make searches of its components, through the document itself, but also through different documents. This is a challenger problem that has motivated different lines of research in the field of Document Analysis at different levels: Pre-processing techniques have been developed to upgrade the quality of the document image, reducing noise from the input devices and minimizing the effects of the degradation of documents. A deep study in segmentation has been carried out in order to separate the regions of interest from the document background. Finally, many descriptors have been proposed for representing and identifying these regions of interest since the end of 60s until now. In this thesis, we have focused on, this last problem, the shape description description and also on classifier fusion, to apply them to one of the application fields in the Document Analysis: the graphics recognition. In shape recognition, many applications have to face the problem of describing a large number of complex shapes for recognition or retrieval in large databases. Besides the large number of shapes, we can find other challenges for shape description, such as the similarity among some of the shapes or the variability of the shape classes. In these cases, one of the key issues is the design of highly discriminant shape descriptors. Unfortunately, one kind of descriptor is not usually enough to achieve satisfactory results and hence, we have to combine the information from different sources to improve the global performance of the recognition system. We have carried out this combination of information using classifier fusion. Concerning shape description, traditionally graphics have been represented using structural descriptors, which are based on a vectorial representation of the shape. Vectorization is quite sensitive to noise and to distortions of sketched symbols. We can try to overcome this problem using grammar descriptors or deformable models of shapes. Another possibility, which is the followed in this dissertation, is to propose descriptors that do not need a vectorial representation of the symbol. Thereby, in the context of shape description, we have proposed a descriptor based on the ridgelets transform which, thanks to we have unified the terminology used in shape description and the introduced vocabulary, we can define as: 2D, polar and multi-resolution descriptor information preserving and invariant to similarities. On the other hand, although ridgelets descriptor can be considered as a single descriptor, it offers a shape representation divided into groups of coefficients, which permit us to consider them as single descriptors. Thus, for each descriptor, we have trained a classifier and we have proposed two linear combination rules, IN and DN, that minimize the classification error of classifiers verifying a set of constraints concerning the dependence and the distribtuion of classifers. These theoretical approaches have been evaluated through an experimental evaluation in ridgelets descriptors, classifier fusion and applying the classifier fusion methods to ridge lets descriptors, obtaining the following results: Ridgelets descriptors have proven to represent graphics symbols better than general purpose descriptors. IN and DN methods reduce the misclassification rates regarding other reference fusion methods. Finally, the IN method applied to ridgelets descriptor, in combination of boosting algorithms, has reached recognition rates near to 100% in the test defined for the GREC'03 database

e-Counterfeit: a mobile-server platform for document counterfeit detection

This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.Comment: 6 pages, 5 figure

A Flexible Outlier Detector Based on a Topology Given by Graph Communities

Acord transformatiu CRUE-CSICOutlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings

Histogram of Radon Transform. A useful descriptor for shape retrieval

International audienceIn this paper we present a new descriptor based on the Radon transform. We propose a histogram of the Radon transform, called HRT, which is invariant to common geometrical transformations. For black and white shapes, the HRT descriptor is a histogram of shape lengths at each orientation. The experimental results, defined on different databases and compared with several well-known descriptors, show the robustness of our method

Document Noise Removal using Sparse Representations over Learned Dictionary

International audienceIn this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art

Symbol descriptor based on shape context and vector model of information retrieval

International audienceIn this paper we present an adaptative method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a large set of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising

New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation

International audienceIn this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learned dictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. The evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods

Noise suppression over bi-level graphical documents by sparse representation

International audienceIn this paper, we explore the use of learning algorithm (K-SVD) for building dictionaries adapted to the image properties. In addition, in our model, we also modeled the energy of the noise basing on the function of the normalized cross-correlation between noised and non noised documents identified in training set. We have evaluated this method on the Grec2005 dataset. The experimental results demonstrate the robustness of our approach by comparing it with state-of-the-art methods.Dans cet article, nous explorons l'utilisation de l'algorithme d'apprentissage (K-SVD) pour construire des dictionnaires adaptés aux documents graphiques. En plus, dans notre modèle, nous avons également modélisé l'énergie du bruit à partir de la fonction de la corrélation croisée normalisée entre les documents bruités et non bruités définis dans notre base d'apprentissage. Nous avons évalué cette méthode sur la base de données Grec2005. Les résultats expérimentaux démontrent la robustesse de notre approche en comparant à des méthodes de l'état de l'art