TRECVID 2007 Collaborative Annotation using Active Learning

Stéphane Ayache (1) , Georges Quénot (2)

1 LIF - Laboratoire d'informatique Fondamentale de Marseille - UMR 6166
2 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]

Concept indexing in multimedia libraries is very useful for users searching and browsing but it is a very challenging research problem as well. Beyond the systems' implementations issues, semantic indexing is strongly dependant upon the size and quality of the training examples. In this paper, we describe the collaborative annotation system used to annotate the High Level Features (HLF) in the development set of TRECVID 2007. This system is web-based and takes advantage of Active Learning approach. We show that Active Learning allows simultaneously getting the most useful information form the partial annotation and significantly reducing the annotation effort per participant relatively to previous collaborative annotations.

Mots clés: Collaborative annotation; active learning; TRECVID

Domaines: Recherche d'information [cs.IR]

Stéphane Ayache, Georges Quénot. TRECVID 2007 Collaborative Annotation using Active Learning. TRECVID Workshop, 2007, Gaithersburg, MD, United States. ⟨hal-00953889⟩