VENDREDI 26 SEPTEMBRE, SALLE DES RESISTANTS, 16H

Herve Jegou (Herve.Jegou@inria.fr)
LEAR Project, INRIA Rhone-Alpes
http://lear.inrialpes.fr/people/jegou/

Title: Hamming Embedding and Weak Geometric consistency for
large-scale image search

Abstract: We address the problem of large scale image search, for
which many recent methods use a bag-of-features image
representation. We shows 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. HE and WGC are
integrated within an inverted file system and are efficiently
exploited even in the case of very large datasets. Experiments
performed on a dataset of one million images show a significant
improvement due to the binary signatures and the weak geometric
consistency constraints, as well as their efficiency. Estimation of
the full geometric transformation, i.e., a re-ranking step on a short
list of images, is complementary to our weak geometric consistency
constraints and allows to further improve the accuracy. This is joint
work with M. Douze and C. Schmid.