RERANKING WITH CONTEXTUAL DISSIMILARITY MEASURES FROM REPRESENTATIONAL BREGMAN K-MEANS
Olivier Schwander, Frank Nielsen · 2010
We present a novel reranking framework for Content Based Image Retrieval (CBIR) systems based on contextual 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.