Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18583
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorGao, Q-
dc.creatorZhang, L-
dc.creatorZhang, D-
dc.creatorXu, H-
dc.date.accessioned2014-12-31T08:01:27Z-
dc.date.available2014-12-31T08:01:27Z-
dc.identifier.issn0167-8655-
dc.identifier.urihttp://hdl.handle.net/10397/18583-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectDirectional imageen_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectIndependent component analysisen_US
dc.titleIndependent components extraction from image matrixen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage171-
dc.identifier.epage178-
dc.identifier.volume31-
dc.identifier.issue3-
dc.identifier.doi10.1016/j.patrec.2009.10.014-
dcterms.abstractThe key problem of extracting independent components (ICs) is to learn the demixing matrix from the known training images which can be unfolded to vectors in conventional independent component analysis (ICA). However, the unfolded vectors lead to the small sample size problem (SSS) and the curse of dimensionality. In this paper, a novel independent feature extraction method is proposed to solve these problems by encoding each input image as a matrix. In addition, the row and column directional images of the matrix are introduced to better exploit the spatial and structural information embedded in image during the training phase. Compared with the conventional ICA, the proposed method directly evaluates the two correlated demixing matrices from the image matrix without matrix-to-vector transformation, greatly alleviates the SSS and the curse of dimensionality, reduces the computational complexity, and simultaneously exploits the spatial and structural information embedded in image. Extensive experiments show that the proposed method is superior to the standard ICA method and some unsupervised methods.-
dcterms.bibliographicCitationPattern recognition letters, 2010, v. 31, no. 3, p. 171-178-
dcterms.isPartOfPattern recognition letters-
dcterms.issued2010-
dc.identifier.isiWOS:000274281600001-
dc.identifier.scopus2-s2.0-72649088706-
dc.identifier.eissn1872-7344-
dc.identifier.rosgroupidr47578-
dc.description.ros2009-2010 > Academic research: refereed > Publication in refereed journal-
Appears in Collections:Journal/Magazine Article
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

SCOPUSTM   
Citations

18
Last Week
0
Last month
0
Citations as of Feb 14, 2020

WEB OF SCIENCETM
Citations

7
Last Week
0
Last month
0
Citations as of Jul 10, 2020

Page view(s)

164
Last Week
6
Last month
Citations as of Feb 12, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.