Product quantization for nearest neighbor search

1 Product quantization for nearest neighbor searchHerv e J egou, Matthijs Douze, Cordelia SchmidAbstract This paper introduces a Product quantizationbased approach for approximate nearest neighbor idea is to decomposes the space into a Cartesianproduct of low dimensional subspaces and to quantize eachsubspace separately. A vector is represented by a shortcode composed of its subspace quantization indices. TheEuclidean distance between two vectors can be efficientlyestimated from their codes. An asymmetric version in-creases precision, as it computes the approximate distancebetween a vector and a results show that our approach searchesfor nearest neighbors efficiently, in particular in combi-nation with an inverted file system. Results for SIFT andGIST image descriptors show excellent search accuracyoutperforming three state-of-the-art approaches.

on the search quality with limited assumptions. It has been successfully used for local descriptors [12] and 3D object indexing [13], [11]. However, for real data, LSH is outperformed by heuristic methods, which exploit the distribution of the vectors. These methods include randomized KD-trees [14] and hierarchical k-means [15],

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  Product, Quality, Search, Easterns, Neighbor, Quantization, Product quantization for nearest neighbor search

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