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We address the problem of image search on a very large scale, where three constraints have to be considered jointly: the accuracy of the search, its efficiency, and the memory usage of the representation. We first propose a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation...
In this paper we present two contributions to improve accuracy and speed of an image search system based on bag-of-features: a contextual dissimilarity measure (CDM) and an efficient search structure for visual word vectors. Our measure (CDM) takes into account the local distribution of the vectors and iteratively estimates distance correcting terms. These terms are subsequently used to update an...
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