Sélection adaptative des dimensions de l'indexation visuelle d'images mal annotées en fonction du mot recherché

The construction of generic visual oncept models is difficult, because the great image databases are not well labeled. This invalidates the traditional optimization methods for the high dimensional visual space. In order to enhance image search and index system we propose novel visual featues based on entropy analysis, and two methods to reduce the features space allowing to estimate the most discriminant visual features for a given keyword. We approximate LDA or MMD on real misslabled image databases. Then, we use a non supervised clustering algorithm to build visual clusters, using all the features of the visual space, or several subspaces made up with the most discriminant features depending of each keyword. Results on COREL show classification enhancement of 69 % while reducing the number of dimensions by 79 %. The collections size impacts for our methods are discussed.