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This paper introduces the contextual dissimilarity measure which significantly improves the accuracy of bag-of-features 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 termsdoi:10.1109/tpami.2008.285 pmid:19926895 fatcat:rhpvp6h7dzbebgkicisgnkm6p4