Stable third-order tensor representation for colour image classification

Tao, D; Maybank, S; Hu, W; Li, X

Stable third-order tensor representation for colour image classification

Tao, D

Maybank, S Hu, W Li, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005, 2005, 2005 pp. 641 - 644
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Maybank, S	en_US
dc.contributor.author	Hu, W	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005, 2005, 2005 pp. 641 - 644	en_US
dc.identifier.isbn	076952415X	en_US
dc.identifier.isbn	9780769524153	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17275
dc.description.abstract	General tensors can represent colour images more naturally than conventional features; however the general tensors ' stability properties are not reported and remain to be a key problem. In this paper, we use the tensor minimax probability (TMPM) to prove that the tensor representation is stable. The proof is based on the random subspace method through a large number of experiments. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005	en_US
dc.relation.isbasedon	10.1109/WI.2005.136	en_US
dc.title	Stable third-order tensor representation for colour image classification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2005	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Compiegne, France	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
pubs.volume	2005	en_US

Abstract:

General tensors can represent colour images more naturally than conventional features; however the general tensors ' stability properties are not reported and remain to be a key problem. In this paper, we use the tensor minimax probability (TMPM) to prove that the tensor representation is stable. The proof is based on the random subspace method through a large number of experiments. © 2005 IEEE.

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http://hdl.handle.net/10453/17275