Accurate Image Retrieval Using Content Dissimilarity: Performance Enhancement by Indexing
This paper introduces the content 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 neighbourhood structure. Experimental results show that our approach gives significantly better results than a standard distance method. 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. 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 aggregation. They are shown to further improve accuracy at the cost of higher memory usage and lower efficiency. We also combine a indexing technique for achieving efficient and effective retrieval performance.
KeywordsImage retrieval Distance regularization Clustering Indexing
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