Unified discriminating feature analysis for visual category recognition

Liu, W; Gao, C; Chang, X; Wu, Q

Unified discriminating feature analysis for visual category recognition

Liu, W Gao, C Chang, X

Wu, Q

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Publisher:: ACADEMIC PRESS INC ELSEVIER SCIENCE
Publication Type:: Journal Article
Citation:: Journal of Visual Communication and Image Representation, 2016, 40, pp. 772-778
Issue Date:: 2016-10-01

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Field	Value	Language
dc.contributor.author	Liu, W
dc.contributor.author	Gao, C
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Wu, Q
dc.date.accessioned	2023-03-31T10:59:50Z
dc.date.available	2023-03-31T10:59:50Z
dc.date.issued	2016-10-01
dc.identifier.citation	Journal of Visual Communication and Image Representation, 2016, 40, pp. 772-778
dc.identifier.issn	1047-3203
dc.identifier.issn	1095-9076
dc.identifier.uri	http://hdl.handle.net/10453/169011
dc.description.abstract	Visual category recognition (VCR) is one of the most important tasks in image and video indexing. To deal with high dimension image/video data, feature analysis algorithms have been widely used for visual category recognition. In this paper, to enhance the flexibility regarding the exploitation of labeled or unlabeled data, we propose a unified feature analysis framework that can be applied to both supervised and semi-supervised scenarios. Furthermore, by revealing intrinsic relationships of traditional feature analysis methods, our framework not only integrates traditional methods, but also introduces an ℓ2,1-norm regularization term for sparse learning. Extensive experiments report that the proposed method obtains advantageous performance in comparison with other state-of-the-art supervised and semi-supervised feature selection algorithms.
dc.language	English
dc.publisher	ACADEMIC PRESS INC ELSEVIER SCIENCE
dc.relation.ispartof	Journal of Visual Communication and Image Representation
dc.relation.isbasedon	10.1016/j.jvcir.2016.06.028
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1203 Design Practice and Management, 1905 Visual Arts and Crafts
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Unified discriminating feature analysis for visual category recognition
dc.type	Journal Article
utslib.citation.volume	40
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1203 Design Practice and Management
utslib.for	1905 Visual Arts and Crafts
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/Students
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T10:59:49Z
pubs.publication-status	Published
pubs.volume	40

Abstract:

Visual category recognition (VCR) is one of the most important tasks in image and video indexing. To deal with high dimension image/video data, feature analysis algorithms have been widely used for visual category recognition. In this paper, to enhance the flexibility regarding the exploitation of labeled or unlabeled data, we propose a unified feature analysis framework that can be applied to both supervised and semi-supervised scenarios. Furthermore, by revealing intrinsic relationships of traditional feature analysis methods, our framework not only integrates traditional methods, but also introduces an ℓ2,1-norm regularization term for sparse learning. Extensive experiments report that the proposed method obtains advantageous performance in comparison with other state-of-the-art supervised and semi-supervised feature selection algorithms.

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http://hdl.handle.net/10453/169011