Event Location: NSH 1507
Bio: Olivier Duchenne received the M.S. degree in Computer Science and Applied Mathematics in
École Normale Supérieure (ENS), in Paris in 2008. He then joined as a phD candidate the research team, WILLOW in the same university under the supervision of professor Jean Ponce. He received the best student paper, honorable mention, at the
conference ieee CVPR 2009, in Miami. His research interests include graph matching, video action recognition and image alignment.

Abstract: This presentation is about our ICCV 2011 article.
In this paper, we address the problem of category-level image
classification. The underlying image model is a graph whose nodes
correspond to a dense set of regions, and edges reflect the underlying
grid structure of the image and act as springs to guarantee the
geometric consistency of nearby regions during matching. A fast
approximate algorithm for matching the graphs associated with two
images is presented. This algorithm is
used to construct a kernel appropriate for SVM-based image
classification, and experiments with the Caltech 101, Caltech 256, and
Scenes datasets demonstrate performance that matches or exceeds the
state of the art for methods using a single type of features.