Accurate Image Search Using the Contextual Dissimilarity Measure

— This paper introduces the contextual dissimilarity measure which significantly improves the accuracy of bag-offeatures based image search. Our measure takes into account the local distribution of the vectors and iteratively estimates distance update terms in the spirit of Sinkhorn’s scaling algorithm, thereby modifying the neighborhood structure. Experimental results show that our approach gives significantly better results than a standard distance and outperforms the state-of-the-art in terms of accuracy on the Nist´er-Stew´enius and Lola datasets. This paper also evaluates the impact of a large number of parameters, including the number of descriptors, the clustering method, the visual vocabulary size and the distance measure. The optimal parameter choice is shown to be quite contextdependent. In particular using a large number of descriptors is interesting only when using our dissimilarity measure. We have also evaluated two novel variants, multiple assignment and rank agg...

• Type: Journal
• Year: 2010
• Where: PAMI
• Authors: Herve Jegou, Cordelia Schmid, Hedi Harzallah, Jakob J. Verbeek
• Keywords: Contextual Dissimilarity Measure, Dissimilarity Measure, Large Number, PAMI 2010