Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12841
Title: Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval
Authors: Xu, D
Yan, S
Tao, D
Lin, S
Zhang, HJ
Keywords: Content-based image retrieval (CBIR)
Dimensionality reduction
Gait recognition
Marginal Fisher analysis (MFA)
Relevance feedback
Issue Date: 2007
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2007, v. 16, no. 11, p. 2811-2821 How to cite?
Journal: IEEE transactions on image processing 
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.
URI: http://hdl.handle.net/10397/12841
ISSN: 1057-7149 (print)
1941-0042 (online)
DOI: 10.1109/TIP.2007.906769
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

176
Last Week
0
Last month
5
Citations as of Apr 25, 2017

WEB OF SCIENCETM
Citations

140
Last Week
0
Last month
5
Citations as of Apr 27, 2017

Page view(s)

45
Last Week
5
Last month
Checked on Apr 23, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.