Event Location: NSH 1507
Bio: Herve Jegou holds a M.S. degree and a PhD in Computer Science from the
University of Rennes. He is a former student of the Ecole Normale
Superieure de Cachan. After being a post-doctoral research assistant in
the INRIA TEXMEX project, he is a full-time researcher at the LEAR
project-team at INRIA Rhone-Alpes, France, since 2006. His research
interests concern large scale image retrieval and approximate nearest
neighbor search.

Abstract: We address the problem of large scale image search, for which many
recent methods use a bag-of-features image representation. We show the
sub-optimality of such a representation for matching descriptors and
derive a more precise representation based on 1) Hamming embedding (HE)
and 2) weak geometric consistency constraints (WGC). HE provides binary
signatures that refine the matching based on visual words. WGC filters
matching descriptors that are not consistent in terms of angle and
scale. Experiments performed on a dataset of one million images show a
significant improvement due to our approach. This is confirmed by the
Trecvid2008 video copyright detection task, where we obtained the best
results in terms of accuracy for all types of transformation.

This is joint work with M. Douze and C. Schmid.