Multiorder polygonal approximation of digital curves

In this paper, we propose a quick threshold-free algorithm, which computes the angular shape of a 2D object from the points of its contour. For that, we have extended the method defined in [4, 5] to a multiorder analysis. It is based on the arithmetical definition of discrete lines [11] with variable thickness. We provide a framework to analyse a digital curve at different levels of thickness. The extremities of a segment provided at a high resolution are tracked at lower resolution in order to refine their location. The method is thresholdfree and automatically provides a partitioning of a digital curve into its meaningful parts

A hypergraph-based model for graph clustering: application to image indexing

Version finale disponible : www.springerlink.comInternational audienceIn this paper, we introduce a prototype-based clustering algorithm dealing with graphs. We propose a hypergraph-based model for graph data sets by allowing clusters overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we define a retrieval technique indexing the database with hyperedge centroids. This model is interesting to travel the data set and efficient to cluster and retrieve graphs

Attributed Graph Matching using Local Descriptions

Version final disponible : www.springerlink.comInternational audienceIn the pattern recognition context, objects can be represented as graphs with attributed nodes and edges involving their relations. Consequently, matching attributed graphs plays an important role in objects recognition. In this paper, a node signatures extraction is combined with an optimal assignment method for matching attributed graphs. In particular, we show how local descriptions are used to define a node-to-node cost in an assignment problem using the Hungarian method. Moreover, we propose a distance formula to compute the distance between attributed graphs. The experiments demonstrate that the newly presented algorithm is well-suited to pattern recognition applications. Compared with well-known methods, our algorithm gives good results for retrieving images

Classification et extension automatique d'annotations d'images en utilisant un réseau Bayésien

National audienceThe rapid growth of Internet and multimedia information has shown a need in the development of multimedia information retrieval techniques, especially in image retrieval. We can distinguish two main trends. The first one, called “text-based image retrieval”, consists in applying text-retrieval techniques from fully annotated images. The text propodescribes high-level concepts but this technique presents some drawbacks: it requires a tedious work of annotation. Moreover, annotations could be ambiguous because two users can use different keywords to describe a same image. Consequently some approaches have proposed to useWordnet in order to reduce these potential ambiguities. The second approach, called “content-based image retrieval” is a younger field. These methods rely on visual features (color, texture or shape) computed automatically, and retrieve images using a similarity measure. However, the obtained performances are not really acceptable, except in the case of well-focused corpus. In order to improve the recognition, a solution consists in combining visual and semantic information. In many vision problems, instead of having fully annotated training data, it is easier to obtain just a subset of data with annotations, because it is less restrictive for the user. This paper deals with modeling, classifying, and annotating weakly annotated images. More precisely, we propose a scheme for image classification optimization, using a joint visual-text clustering approach and automatically extending image annotations. The proposed approach is derived from the probabilistic graphical model theory and dedicated for both tasks of weakly-annotated image classification and annotation. We consider an image as weakly annotated if the number of keywords defined for it is less than the maximum defined in the ground truth. Thanks to their ability to manage missing values, a probabilistic graphical model has been proposed to represent weakly annotated images. We propose a probabilistic graphical model based on a Gaussian-Mixtures and Multinomial mixture. The visual features are estimated by the Gaussian mixtures and the keywords by a Multinomial distribution. Therefore, the proposed model does not require that all images be annotated: when an image is weakly annotated, the missing keywords are considered as missing values. Besides, our model can automatically extend existing annotations to weakly-annotated images, without user intervention. The uncertainty around the association between a set of keywords and an image is tackled by a joint probability distribution (defined from Gaussian-Mixtures and Multinomial mixture) over the dictionary of keywords and the visual features extracted from our collection of images. Moreover, in order to solve the dimensionality problem due to the large dimensions of visual features, we have adapted a variable selection method. Results of visual-textual classification, reported on a database of images collected from the Web, partially and manually annotated, show an improvement of about 32.3% in terms of recognition rate against only visual information classification. Besides the automatic annotation extension with our model for images with missing keywords outperforms the visual-textual classification of about 6.8%. Finally the proposed method is experimentally competitive with the state-of-art classifiers.Nous proposons, dans cet article, d'améliorer la classification d'images, en utilisant une approche de classification visuo-textuelle (à base de caractéristiques visuelles et textuelles), et en étendant automatiquement les annotations existantes aux images non annotées. L'approche proposée est dérivée de la théorie des modèles graphiques probabilistes et dédiée aux deux tâches de classification et d'annotation d'images partiellement annotées. Nous considérons une image comme partiellement annotée si elle ne possède pas le nombre maximal de mots-clés disponibles par image dans la vérité-terrain. Grâce à leur capacité à fonctionner en présence de données manquantes, un modèle graphique probabiliste a été proposé pour représenter les images partiellement annotées. Ce modèle est basé sur un mélange de lois multinomiales et de mélanges de Gaussiennes. La distribution des caractéristiques visuelles est estimée par des mélanges de Gaussiennes et celle des mots-clés par une loi multinomiale. Par conséquent, le modèle proposé ne requiert pas que toutes les images soient annotées : lorsqu'une image est partiellement annotées, les mots-clés manquants sont considérés comme des valeurs manquantes. De plus, notre modèle peut automatiquement étendre des annotations existantes à des images partiellement annotées, sans l'intervention de l'utilisateur. L'incertitude autour de l'association entre un ensemble de mots-clés et une image est capturée par une distribution de probabilité jointe (définie par un mélange de lois multinomiales et de mélanges de Gaussiennes) sur le dictionnaire de mots-clés et les caractéristiques visuelles extraites de notre collection d'images. De plus, de façon à résoudre le problème de dimensionnalité dû à la grande dimension des caractéristiques visuelles, nous avons adapté une méthode de sélection de variables. Les résultats de la classification visuo-textuelle, obtenus sur une base d'images collectées sur Internet, partiellement et manuellement annotée, montrent une amélioration de 32.3 % en terme de taux de reconnaissance, par rapport à la classification basée sur l'information visuelle uniquement. Par ailleurs, l'extension automatique d'annotations, avec notre modèle, sur des images avec mots-clés manquants, améliore encore la classification visuo-textuelle de 6.8 %. Enfin, la méthode proposée s'est montrée compétitive avec des classificateurs de l'état de l'art

Fast generic polar harmonic transforms

International audienceGeneric polar harmonic transforms have recently been proposed to extract rotation-invariant features from images and their usefulness has been demonstrated in a number of pattern recognition problems. However, direct computation of these transforms from their definition is inefficient and is usually slower than some efficient computation strategies that have been proposed for other methods. This paper presents a number of novel computation strategies to compute these transforms rapidly. The proposed methods are based on the inherent recurrence relations among complex exponential and trigonometric functions used in the definition of the radial and angular kernels of these transforms. The employment of these relations leads to recursive and addition chain-based strategies for fast computation of harmonic function-based kernels. Experimental results show that the proposed method is about 10× faster than direct computation and 5× faster than fast computation of Zernike moments using the q-recursive strategy. Thus, among all existing rotation-invariant feature extraction methods, polar harmonic transforms are the fastest

Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval

International audienceIn many vision problems, instead of having fully annotated training data, it is easier to obtain just a subset of data with annotations, because it is less restrictive for the user. For this reason, in this paper, we consider especially the problem of weakly-annotated image retrieval, where just a small subset of the database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle missing values and to capture the user's preference by also considering a relevance feedback process. Results of visual-textual retrieval associated to a relevance feedback process, reported on a database of images collected from the Web, partially and manually annotated, show an improvement of about 44.5% in terms of recognition rate against content-based retrieval

Une méthode de localisation et de reconnaissance de symboles sans connaissance a priori

International audienceNous présentons ici une méthode pour segmenter des symboles dans des documents graphiques, sans aucune connaissance a priori sur les symboles. Ce système se base sur une méthode de description structurelle qui va permettre de mettre en avant un certain de nombre de régions pouvant contenir un symbole. A partir d'un découpage du document en chaînes de points clés, nous proposons de fusionner successivement les régions entre elles, en fonction d'un critère de densité. Cela nous permet de «reconstruire» les symboles et d'effectuer une reconnaissance à partir d'une requête présentée par l'utilisateur

A progressive learning method for symbols recognition

Conditional discriminant analysis, symbol recognitionInternational audienceThis paper deals with a progressive learning method for symbols recognition which improves its own recognition rate when new symbols are recognized in graphics documents. We propose a discriminant analysis method which provides allocation rules from learning samples with known classes. However a discriminant analysis method is efficient only if learning samples and data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each observation to take into account the parasitic effects between the data and the learning samples. We propose also an adaptation to consider the user feedback

A Bayesian network for combining descriptors: application to symbol recognition

International audienceIn this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shapes measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach

Classification de symboles avec un treillis de Galois et une représentation par sac de mots

National audienceThis paper presents a new approach for graphical symbols recognition by combining a concept lattice with a bag of words representation. Visual words define the properties of a graphical symbol that will be modeled in the Galois Lattice. Indeed, the algorithm of classification is based on the Galois lattice where the intentions of its concepts are the visual words. The use of words as visual primitives allows to evaluate the classifier with a symbolic approach that no longer need the step of the signature discretization to build the Galois Lattice. Our approach is compared to classical approaches without a bag of words and to classical classifiers which are evaluated on different symbols. We show the relevance and the robustness of our approach for graphics recognition.Cet article présente une nouvelle approche pour la reconnaissance de symboles graphiques en combinant un treillis de concepts avec une représentation par sac de mots. Les mots visuels définissent les propriétés représentatives d'un symbole graphique qui seront modélisées dans le treillis de Galois. En effet, l'algorithme de classification est fondé sur le treillis de Galois où les intentions de ses concepts représentent des mots visuels. L'utilisation des mots visuels comme des primitives permet d'évaluer le classifieur avec une approche symbolique qui n'a plus besoin de l' étape de discrétisation primordiale pour la construction du treillis. Notre méthode est comparée aux approches classiques, sans sac de mots et à plusieurs classifieurs usuels, évalués sur différents symboles. Nous montrons la pertinence et la robustesse de notre proposition pour la classification de symboles graphiques