Cross-view feature hashing for image retrieval

Wu, W; Li, B; Chen, L; Zhang, C

Cross-view feature hashing for image retrieval

Wu, W Li, B Chen, L Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9651 pp. 203 - 214
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Wu, W	en_US
dc.contributor.author	Li, B	en_US
dc.contributor.author	Chen, L	en_US
dc.contributor.author	Zhang, C	en_US
dc.date	2016-04-19	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9651 pp. 203 - 214	en_US
dc.identifier.isbn	9783319317526	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43955
dc.description.abstract	© Springer International Publishing Switzerland 2016.Traditional cross-view information retrieval mainly rests on correlating two sets of features in different views. However, features in different views usually have different physical interpretations. It may be inappropriate to map multiple views of data onto a shared feature space and directly compare them. In this paper, we propose a simple yet effective Cross-View Feature Hashing (CVFH) algorithm via a “partition and match” approach. The feature space for each view is bi-partitioned multiple times using B hash functions and the resulting binary codes for all the views can thus be represented in a compatible B-bit Hamming space. To ensure that hashed feature space is effective for supporting generic machine learning and information retrieval functionalities, the hash functions are learned to satisfy two criteria: (1) the neighbors in the original feature spaces should be also close in the Hamming space; and (2) the binary codes for multiple views of the same sample should be similar in the shared Hamming space. We apply CVFH to cross view image retrieval. The experimental results show that CVFH can outperform the Canonical Component Analysis (CCA) based cross-view method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140100545
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.ispartof	Pacific-Asia Conference on Knowledge Discovery and Data Mining	en_US
dc.relation.isbasedon	10.1007/978-3-319-31753-3_17	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Cross-view feature hashing for image retrieval	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9651	en_US
utslib.location.activity	Auckland, New Zealand	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	08 Information And Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2016-04-22	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-04-19	en_US
pubs.volume	9651	en_US

Abstract:

© Springer International Publishing Switzerland 2016.Traditional cross-view information retrieval mainly rests on correlating two sets of features in different views. However, features in different views usually have different physical interpretations. It may be inappropriate to map multiple views of data onto a shared feature space and directly compare them. In this paper, we propose a simple yet effective Cross-View Feature Hashing (CVFH) algorithm via a “partition and match” approach. The feature space for each view is bi-partitioned multiple times using B hash functions and the resulting binary codes for all the views can thus be represented in a compatible B-bit Hamming space. To ensure that hashed feature space is effective for supporting generic machine learning and information retrieval functionalities, the hash functions are learned to satisfy two criteria: (1) the neighbors in the original feature spaces should be also close in the Hamming space; and (2) the binary codes for multiple views of the same sample should be similar in the shared Hamming space. We apply CVFH to cross view image retrieval. The experimental results show that CVFH can outperform the Canonical Component Analysis (CCA) based cross-view method.

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http://hdl.handle.net/10453/43955