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Article: Block linear discriminant analysis for visual tensor objects with frequency or time information

TitleBlock linear discriminant analysis for visual tensor objects with frequency or time information
Authors
KeywordsWithin-class scatter
Between-class scatter
Discriminant analysis
Gait recognition
Hyperspectral face recognition
Visual tensors
Issue Date2017
Citation
Journal of Visual Communication and Image Representation, 2017, v. 49, p. 38-46 How to Cite?
Abstract© 2017 Elsevier Inc. Recently, due to the advancement of acquisition techniques, visual tensor data have been accumulated in a great variety of engineering fields, e.g., biometrics, neuroscience, surveillance and remote sensing. How to analyze and learn with such tensor objects thus becomes an important and growing interest in machine learning community. In this paper, we propose a block linear discriminant analysis (BLDA) algorithm to extract features for visual tensor objects such as multichannel/hyperspectral face images or human gait videos. Taking the inherent characteristic of such tensor data into account, we unfold tensor objects according to their spatial information and frequency/time information, and represent them in a block matrix form. As a result, the block form between-class and within-class scatter matrices are constructed, and a related block eigen-decomposition is solved to extract features for classification. Comprehensive experiments have been carried out to test the effectiveness of the proposed method, and the results show that BLDA outperforms existing algorithms like DATER, 2DLDA, GTDA, UMLDA, STDA and MPCA for visual tensor object analysis.
Persistent Identifierhttp://hdl.handle.net/10722/276544
ISSN
2017 Impact Factor: 1.836
2015 SCImago Journal Rankings: 0.785

 

DC FieldValueLanguage
dc.contributor.authorLi, Xutao-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorYe, Yunming-
dc.contributor.authorWang, Eric Ke-
dc.contributor.authorXu, Xiaofei-
dc.date.accessioned2019-09-18T08:33:56Z-
dc.date.available2019-09-18T08:33:56Z-
dc.date.issued2017-
dc.identifier.citationJournal of Visual Communication and Image Representation, 2017, v. 49, p. 38-46-
dc.identifier.issn1047-3203-
dc.identifier.urihttp://hdl.handle.net/10722/276544-
dc.description.abstract© 2017 Elsevier Inc. Recently, due to the advancement of acquisition techniques, visual tensor data have been accumulated in a great variety of engineering fields, e.g., biometrics, neuroscience, surveillance and remote sensing. How to analyze and learn with such tensor objects thus becomes an important and growing interest in machine learning community. In this paper, we propose a block linear discriminant analysis (BLDA) algorithm to extract features for visual tensor objects such as multichannel/hyperspectral face images or human gait videos. Taking the inherent characteristic of such tensor data into account, we unfold tensor objects according to their spatial information and frequency/time information, and represent them in a block matrix form. As a result, the block form between-class and within-class scatter matrices are constructed, and a related block eigen-decomposition is solved to extract features for classification. Comprehensive experiments have been carried out to test the effectiveness of the proposed method, and the results show that BLDA outperforms existing algorithms like DATER, 2DLDA, GTDA, UMLDA, STDA and MPCA for visual tensor object analysis.-
dc.languageeng-
dc.relation.ispartofJournal of Visual Communication and Image Representation-
dc.subjectWithin-class scatter-
dc.subjectBetween-class scatter-
dc.subjectDiscriminant analysis-
dc.subjectGait recognition-
dc.subjectHyperspectral face recognition-
dc.subjectVisual tensors-
dc.titleBlock linear discriminant analysis for visual tensor objects with frequency or time information-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jvcir.2017.08.004-
dc.identifier.scopuseid_2-s2.0-85026915794-
dc.identifier.volume49-
dc.identifier.spage38-
dc.identifier.epage46-
dc.identifier.eissn1095-9076-

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