Utilisation du contexte pour l'indexation sémantique des images et vidéos. (Using context for semantic indexing of images and videos)

The automated indexing of image and video is a difficult problem because of the“distance” between the arrays of numbers encoding these documents and the concepts (e.g. people, places, events or objects) with which we wish to annotate them. Methods exist for this but their results are far from satisfactory in terms of generality and accuracy. Existing methods typically use a single set of such examples and consider it as uniform. This is not optimal because the same concept may appear in various contexts and its appearance may be very different depending upon these contexts. In this thesis, we considered the use of context for indexing multimedia documents. The context has been widely used in the state of the art to treat various problems. In our work, we use relationships between concepts as a source of semantic context. For the case of videos, we exploit the temporal context that models relationships between the shots of the same video. We propose several approaches using both types of context and their combination, in different levels of an indexing system. We also present the problem of multiple concept detection. We assume that it is related to the context use problematic. We consider that detecting simultaneously a set of concepts is equivalent to detecting one or more concepts forming the group in a context where the others are present. To do that, we studied and compared two types of approaches. All our proposals are generic and can be applied to any system for the detection of any concept. We evaluated our contributions on TRECVID and VOC collections, which are of international standards and recognized by the community. We achieved good results comparable to those of the best indexing systems evaluated in recent years in the evaluation campaigns cited previously.