Apprentissage Actif avec une Méthode de Réordonnancement pour l'Indexation et la Recherche de Vidéos

Video retrieval can be done by ranking the samples according to their probability scores that were produced by classifiers. It is often possible to improve the retrieval performance by re-ranking the samples. In this paper, we proposed such a method and we combined this method with active learning for video indexing. Experimental results showed that the proposed re-ranking method was able to improve the system performance with about 16-22% in average on TRECVID 2010 semantic indexing task. Furthermore, it improved significantly the performance of the video indexing system based active learning; by considering the Area Under Curve (AUC) as a metric measure for the performance of the active learning, our re-ranking method improved the performance with about 20% in average on TRECVID 2007. MOTS-CLÉS : Indexation et de recherche des vidéos, Apprentissage Actif, Réordonnancement