Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1201
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dc.contributorDepartment of Computing-
dc.creatorZuo, W-
dc.creatorWang, K-
dc.creatorZhang, DD-
dc.date.accessioned2014-12-11T08:27:18Z-
dc.date.available2014-12-11T08:27:18Z-
dc.identifier.isbn0-7803-9134-9-
dc.identifier.urihttp://hdl.handle.net/10397/1201-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectPCAen_US
dc.subject2DPCAen_US
dc.subjectImage recognitionen_US
dc.subjectFace recognitionen_US
dc.subjectPalmprint recognitionen_US
dc.titleBi-directional PCA with assembled matrix distance metricen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dc.description.otherinformationTitle in original file: Bi-dierectional PCA with assembled matrix distance metricen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractPrincipal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCA-based methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose Bi-Directional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy, v. 2, p. 958-961-
dcterms.issued2005-
dc.relation.ispartofbook2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy-
dc.relation.conferenceIEEE International Conference on Image Processing [ICIP]-
dc.identifier.rosgroupidr28174-
dc.description.ros2005-2006 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Conference Paper
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