Structuration de bases multimédia pour une exploration visuelle. (Structuring multimedia bases for visual exploration)

The large increase in multimedia data volume requires the development of effective solutions for visual exploration of multimedia databases. After reviewing the visualization process involved, we emphasis the need of data structuration. The main objective of this thesis is to propose and study clustering and classification of multimedia database for their visual exploration. We begin with a state of the art detailing the data and the metrics we can produce according to the nature of the variables describing each document. Follows a review of the projection and classification techniques. We also present in detail the Spectral Clustering method. Our first contribution is an original method that produces fusion of metrics using rank correlations. We validate this method on an animation movie database coming from an international festival. Then we propose a supervised classification method based on rank correlation. This contribution is evaluated on a multimedia challenge dataset. Then we focus on Spectral Clustering methods. We test a supervised Spectral Clustering technique and compare to state of the art methods. Finally we examine active semi-supervised Spectral Clustering methods. In this context, we propose and validate constraint propagation techniques and strategies to improve the convergence of these active methods. Mots clefs : Structuration, bases multimédia, production de métrique, corrélation de rang, classification, Clustering Spectral, supervision, semi-supervision.