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Product quantization for nearest neighbor search

IEEE transactions on pattern analysis and machine intelligence, 2011-01, Vol.33 (1), p.117-128 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0162-8828 ;EISSN: 1939-3539 ;DOI: 10.1109/TPAMI.2010.57 ;PMID: 21088323

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Title:
Product quantization for nearest neighbor search
Author: Jégou, Hervé ; Douze, Matthijs ; Schmid, Cordelia
Subjects: Algorithms ; Artificial Intelligence ; Cluster Analysis ; Computer Science ; Computer Vision and Pattern Recognition ; Image Interpretation, Computer-Assisted - methods ; Image Processing, Computer-Assisted - methods ; Information Storage and Retrieval ; Models, Statistical ; Pattern Recognition, Automated - methods
Is Part Of: IEEE transactions on pattern analysis and machine intelligence, 2011-01, Vol.33 (1), p.117-128
Description: This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.
Publisher: United States: Institute of Electrical and Electronics Engineers
Language: English
Identifier: ISSN: 0162-8828
EISSN: 1939-3539
DOI: 10.1109/TPAMI.2010.57
PMID: 21088323
Source: Hyper Article en Ligne (HAL) (Open Access)

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Product quantization for nearest neighbor search

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0162-8828 ;EISSN: 1939-3539 ;DOI: 10.1109/TPAMI.2010.57 ;PMID: 21088323

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