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Accurate Image Search Using the Contextual Dissimilarity Measure
2010
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 terms
doi:10.1109/tpami.2008.285
pmid:19926895
fatcat:rhpvp6h7dzbebgkicisgnkm6p4