Learning to multimodal hash for robust video copy detection

Peng, H; Deng, C; An, L; Gao, X; Tao, D

Learning to multimodal hash for robust video copy detection

Peng, H Deng, C An, L Gao, X Tao, D

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 4482 - 4486
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Peng, H	en_US
dc.contributor.author	Deng, C	en_US
dc.contributor.author	An, L	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 4482 - 4486	en_US
dc.identifier.isbn	9781479923410	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28033
dc.description.abstract	Content-based video copy detection (CBVCD) has attracted increasing attention in recent years. However, video content description and search efficiency are still two challenges in this domain. To cope with these two problems, this paper proposes a novel CBVCD approach with similarity preserving multimodal hash learning (SPM2H). The pre-processed video keyframes are represented as multiple features from different perspectives. SPM 2H integrates the multimodal feature fusion and the hashing function learning into a joint framework. Mapping video keyframes into hash codes can conducts fast similarity search in the Hamming space. The experiments show that our approach achieves good performance in accuracy as well as efficiency. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICIP.2013.6738923	en_US
dc.title	Learning to multimodal hash for robust video copy detection	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Content-based video copy detection (CBVCD) has attracted increasing attention in recent years. However, video content description and search efficiency are still two challenges in this domain. To cope with these two problems, this paper proposes a novel CBVCD approach with similarity preserving multimodal hash learning (SPM2H). The pre-processed video keyframes are represented as multiple features from different perspectives. SPM 2H integrates the multimodal feature fusion and the hashing function learning into a joint framework. Mapping video keyframes into hash codes can conducts fast similarity search in the Hamming space. The experiments show that our approach achieves good performance in accuracy as well as efficiency. © 2013 IEEE.

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http://hdl.handle.net/10453/28033