Critical parameters of the sparse representation-based classifier

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dc.authoridBattini Sonmez, Elena/0000-0003-0090-984X|Varlı, Songül/0000-0002-1786-6869
dc.authorwosidBattini Sonmez, Elena/AAZ-6358-2021
dc.authorwosidAlbayrak, Songül/G-5329-2011
dc.authorwosidVarlı, Songül/AAZ-4672-2020
dc.contributor.authorSonmez, Elena Battini
dc.contributor.authorAlbayrak, Songul
dc.date.accessioned2024-07-18T20:56:59Z
dc.date.available2024-07-18T20:56:59Z
dc.date.issued2013
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn recent years, the growing attention in the study of the compressive sensing (CS) theory suggested a novel classification algorithm called sparse representation-based classifier (SRC), which obtained promising results by casting classification as a sparse representation problem. Whereas SRC has been applied to different fields of applications and several variations of it have been proposed, less attention has been given to its critical parameters, that is, measurements correlated to its performance. This work underlines the differences between CS and SRC, it gives a mathematical definition of five measurements possible correlated to the performance of SRC and identifies three of them as critical parameters. The knowledge of the critical parameters is necessary to fuse multiple scores of SRC classifiers allowing for classification. The authors addressed the problem of two-dimensional face classification: using the Extended Yale B dataset to monitor the critical parameters and the Extended Cohn-Kanade database to test the robustness of SRC with emotional faces. Finally, the authors increased the initial performance of the holistic SRC with a block-based SRC, which uses one critical parameter for automatic selection of the most successful blocks.en_US
dc.identifier.doi10.1049/iet-cvi.2012.0127
dc.identifier.endpage507en_US
dc.identifier.issn1751-9632
dc.identifier.issn1751-9640
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-84890173099en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage500en_US
dc.identifier.urihttps://doi.org/10.1049/iet-cvi.2012.0127
dc.identifier.urihttps://hdl.handle.net/11411/8936
dc.identifier.volume7en_US
dc.identifier.wosWOS:000328326400009en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInst Engineering Technology-Ieten_US
dc.relation.ispartofIet Computer Visionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFace Recognitionen_US
dc.subjectİmage Classificationen_US
dc.subjectCompressed Sensingen_US
dc.subjectSparse Representation-Based Classifieren_US
dc.subjectCompressive Sensing Theoryen_US
dc.subjectBlock-Based Srcen_US
dc.subjectHolistic Srcen_US
dc.subjectEmotional Faceen_US
dc.subjectExtended Cohn-Kanade Databaseen_US
dc.subjectExtended Yale B Dataseten_US
dc.subjectTwo-Dimensional Face Classificationen_US
dc.subjectSrc Classifiersen_US
dc.subjectCsen_US
dc.subjectFace Recognitionen_US
dc.subjectRobusten_US
dc.titleCritical parameters of the sparse representation-based classifieren_US
dc.typeArticleen_US

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