A Survey on Learning to Hash

Wang, J; Zhang, T; Song, J; Sebe, N; Shen, HT

A Survey on Learning to Hash

Wang, J Zhang, T Song, J Sebe, N Shen, HT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (4), pp. 769 - 790
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhang, T	en_US
dc.contributor.author	Song, J	en_US
dc.contributor.author	Sebe, N	en_US
dc.contributor.author	Shen, HT https://orcid.org/0000-0002-2999-2088	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (4), pp. 769 - 790	en_US
dc.identifier.issn	0162-8828	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132735
dc.description.abstract	© 1979-2012 IEEE. Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.	en_US
dc.relation.ispartof	IEEE Transactions on Pattern Analysis and Machine Intelligence	en_US
dc.relation.isbasedon	10.1109/TPAMI.2017.2699960	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A Survey on Learning to Hash	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	40	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	40	en_US

Abstract:

© 1979-2012 IEEE. Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.

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http://hdl.handle.net/10453/132735