Indexation de structures de documents par réseaux bayésiens

Our objective is to study the contribution of naive increased Bayesian networks in problems of image classification. The images used in this study represent the structure of a document containing text blocks and graphics. We proposed three variants of Bayesian networks. First naive Bayesian networks RN who, despite their simple structure and strong assumption on independence have given very good results. Secondly, the naive Bayesian networks augmented by a tree TAN. Indeed, the assumption of independence among attributes is in general false. Thus, there are different techniques to relax this assumption. Thirdly, the naive Bayesian networks augmented by a forest called FAN who they are rather well known classification problems have not been investigated to our knowledge in image classification. The results showed a marked improvement over the FAN network RN and TAN.

[1]  Suh-Yin Lee,et al.  2D C-Tree Spatial Representation for Iconic Image , 1999, J. Vis. Lang. Comput..

[2]  Wenyin Liu,et al.  A Discriminative Representation for Symbolic Image Similarity Evaluation , 2007, GREC.

[3]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[4]  Euripides G. M. Petrakis,et al.  Design and evaluation of spatial similarity approaches for image retrieval , 2002, Image Vis. Comput..

[5]  Linda Smail,et al.  Algorithmique pour les Réseaux Bayésiens et leurs extensions , 2004 .

[6]  Olivier François,et al.  De l'identification de structure de réseaux bayésiens à la reconnaissance de formes à partir d'informations complètes ou incomplètes. (Bayesian Network Identification from Complete or Incomplete Datasets) , 2006 .

[7]  Jesús Cerquides,et al.  Tractable Bayesian Learning of Tree Augmented Naive Bayes Classifiers , 2003 .

[8]  Olivier François,et al.  Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets , 2006, Probabilistic Graphical Models.

[9]  Les Relations Spatiales : de la modélisation à la mise en oeuvre , 2008 .

[10]  Xiao Li December.  Augmented Naive Bayesian Classifiers for Mixed-Mode Data , 2003 .

[11]  David Maxwell Chickering,et al.  Learning Bayesian Networks is , 1994 .

[12]  Philippe Leray,et al.  Réseaux bayésiens : Apprentissage et diagnostic de systemes complexes , 2006 .

[13]  Nir Friedman,et al.  Building Classifiers Using Bayesian Networks , 1996, AAAI/IAAI, Vol. 2.