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- Publisher Website: 10.1109/TIP.2007.906769
- Scopus: eid_2-s2.0-36348982900
- PMID: 17990757
- WOS: WOS:000250241500017
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Article: Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval
Title | Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval |
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Authors | |
Keywords | Content-based image retrieval (CBIR) Dimensionality reduction Gait recognition Marginal Fisher analysis (MFA) Relevance feedback |
Issue Date | 2007 |
Citation | IEEE Transactions on Image Processing, 2007, v. 16, n. 11, p. 2811-2821 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/321337 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Yan, Shuicheng | - |
dc.contributor.author | Tao, Daocheng | - |
dc.contributor.author | Lin, Stephen | - |
dc.contributor.author | Zhang, Hong Jiang | - |
dc.date.accessioned | 2022-11-03T02:18:14Z | - |
dc.date.available | 2022-11-03T02:18:14Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2007, v. 16, n. 11, p. 2811-2821 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321337 | - |
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. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | Content-based image retrieval (CBIR) | - |
dc.subject | Dimensionality reduction | - |
dc.subject | Gait recognition | - |
dc.subject | Marginal Fisher analysis (MFA) | - |
dc.subject | Relevance feedback | - |
dc.title | Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2007.906769 | - |
dc.identifier.pmid | 17990757 | - |
dc.identifier.scopus | eid_2-s2.0-36348982900 | - |
dc.identifier.volume | 16 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 2811 | - |
dc.identifier.epage | 2821 | - |
dc.identifier.isi | WOS:000250241500017 | - |