Partial Hash Update via Hamming Subspace Learning

Ma, C; Tsang, IW; Peng, F; Liu, C

Partial Hash Update via Hamming Subspace Learning

Ma, C Tsang, IW

Peng, F Liu, C

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2017, 26 (4), pp. 1939 - 1951
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Ma, C	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Peng, F	en_US
dc.contributor.author	Liu, C	en_US
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2017, 26 (4), pp. 1939 - 1951	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125419
dc.description.abstract	© 2017 IEEE. Hashing technique has become an effective method for information retrieval due to the fast calculation of the Hamming distance. However, with the continuous growth of data coming from the Internet, the online update of hashing on the massive social data becomes very time-consuming. To alleviate this issue, in this paper, we propose a novel updating technique for hashing methods, namely Hamming Subspace Learning (HSL). The motivation of HSL is to generate a low-dimensional Hamming subspace from a high-dimensional Hamming space by selecting representative hash functions. Through HSL, we aim to improve the speed of updating binary codes for all samples. We present a method for Hamming subspace learning based on greedy selection strategy and the Distribution Preserving Hamming Subspace learning (DHSL) algorithm by designing a novel loss function. The experimental results demonstrate that the HSL is effective to improve the speed of online updating and the performance of hashing algorithm.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT130100746
dc.relation	http://purl.org/au-research/grants/arc/LP150100671
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2017.2675342	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Partial Hash Update via Hamming Subspace Learning	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 IEEE. Hashing technique has become an effective method for information retrieval due to the fast calculation of the Hamming distance. However, with the continuous growth of data coming from the Internet, the online update of hashing on the massive social data becomes very time-consuming. To alleviate this issue, in this paper, we propose a novel updating technique for hashing methods, namely Hamming Subspace Learning (HSL). The motivation of HSL is to generate a low-dimensional Hamming subspace from a high-dimensional Hamming space by selecting representative hash functions. Through HSL, we aim to improve the speed of updating binary codes for all samples. We present a method for Hamming subspace learning based on greedy selection strategy and the Distribution Preserving Hamming Subspace learning (DHSL) algorithm by designing a novel loss function. The experimental results demonstrate that the HSL is effective to improve the speed of online updating and the performance of hashing algorithm.

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http://hdl.handle.net/10453/125419