Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval

Xu, D; Yan, S; Tao, D; Lin, S; Zhang, HJ

Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval

Xu, D

Yan, S Tao, D

Lin, S Zhang, HJ

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2007, 16 (11), pp. 2811 - 2821
Issue Date:: 2007-11-01

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Field	Value	Language
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Yan, S	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Lin, S	en_US
dc.contributor.author	Zhang, HJ	en_US
dc.date.issued	2007-11-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2007, 16 (11), pp. 2811 - 2821	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15160
dc.description.abstract	Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications. © 2007 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2007.906769	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Joints	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Photography	en_US
dc.subject.mesh	Image Enhancement	en_US
dc.subject.mesh	Subtraction Technique	en_US
dc.subject.mesh	Gait	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Biometry	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Information Storage and Retrieval	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	16	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
utslib.copyright.status	closed_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications. © 2007 IEEE.

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http://hdl.handle.net/10453/15160