CMU talk: Hamming Embedding and Weak Geometric consistency for large-scale image and video search

CMU VASC Seminar
Monday, December 8, 2008

Hamming Embedding and Weak Geometric consistency for large-scale image and video search
Herve Jegou
INRIA

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.

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.