Large margin image set representation and classification

Handle URI:
http://hdl.handle.net/10754/556534
Title:
Large margin image set representation and classification
Authors:
Wang, Jim Jing-Yan; Alzahrani, Majed A.; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
In 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
IEEE
Journal:
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference/Event name:
2014 International Joint Conference on Neural Networks, IJCNN 2014
Issue Date:
6-Jul-2014
DOI:
10.1109/IJCNN.2014.6889378
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6889378; http://arxiv.org/abs/1404.5588
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorAlzahrani, Majed A.en
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-06-07T21:43:53Zen
dc.date.available2015-06-07T21:43:53Zen
dc.date.issued2014-07-06en
dc.identifier.doi10.1109/IJCNN.2014.6889378en
dc.identifier.urihttp://hdl.handle.net/10754/556534en
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.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6889378en
dc.relation.urlhttp://arxiv.org/abs/1404.5588en
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.en
dc.titleLarge margin image set representation and classificationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNeural Networks (IJCNN), 2014 International Joint Conference onen
dc.conference.date2014-07-06 to 2014-07-11en
dc.conference.name2014 International Joint Conference on Neural Networks, IJCNN 2014en
dc.conference.locationBeijing, CHNen
dc.eprint.versionPost-printen
dc.contributor.institutionthe University at Buffalo, The State University of New York, Buffalo, NY 14203, USAen
dc.contributor.institutionthe Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Chinaen
dc.identifier.arxividarXiv:1404.5588en
kaust.authorGao, Xinen
kaust.authorAlzahrani, Majed A.en
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