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dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorAlzahrani, Majed A.
dc.contributor.authorGao, Xin
dc.date.accessioned2015-06-07T21:43:53Z
dc.date.available2015-06-07T21:43:53Z
dc.date.issued2014-09-10
dc.identifier.doi10.1109/IJCNN.2014.6889378
dc.identifier.urihttp://hdl.handle.net/10754/556534
dc.description.abstractIn this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation - maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6889378
dc.relation.urlhttp://arxiv.org/abs/1404.5588
dc.rights(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleLarge margin image set representation and classification
dc.typeConference Paper
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journal2014 International Joint Conference on Neural Networks (IJCNN)
dc.conference.date2014-07-06 to 2014-07-11
dc.conference.name2014 International Joint Conference on Neural Networks, IJCNN 2014
dc.conference.locationBeijing, CHN
dc.eprint.versionPost-print
dc.contributor.institutionthe University at Buffalo, The State University of New York, Buffalo, NY 14203, USA
dc.contributor.institutionthe Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China
dc.identifier.arxividarXiv:1404.5588
kaust.personGao, Xin
kaust.personAlzahrani, Majed A.
dc.versionv1
refterms.dateFOA2018-06-14T09:32:46Z
dc.date.published-online2014-09-10
dc.date.published-print2014-07
dc.date.posted2014-04-22


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