Sciweavers

Share

Publication

Reranking with Contextual dissimilarity measures from representational Bregman k-means

4 years 5 months ago
Reranking with Contextual dissimilarity measures from representational Bregman k-means
We present a novel reranking framework for Content Based Image Retrieval (CBIR) systems based on con-textual dissimilarity measures. Our work revisit and extend the method of Perronnin et al. (Perronnin et al., 2009) which introduces a way to build contexts used in turn to design contextual dissimilarity measures for reranking. Instead of using truncated rank lists from a CBIR engine as contexts, we rather use a clustering algorithm to group similar images from the rank list. We introduce the representational Bregman divergences and further generalize the Bregman k-means clustering by considering an embedding representation. These representation functions allows one to interpret α-divergences/projections as Bregman divergences/projections on α-representations. Finally, we validate our approach by presenting some experimental results on ranking performances on the INRIA Holidays database.
Olivier Schwander, Frank Nielsen
Added 31 Mar 2010
Updated 31 Mar 2010
Type Conference
Year 2010
Where VISAPP 2010
Authors Olivier Schwander, Frank Nielsen
Comments (0)
books