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Conference Paper: Feature selection in source camera identification

TitleFeature selection in source camera identification
Authors
Issue Date2007
Citation
Conference Proceedings - Ieee International Conference On Systems, Man And Cybernetics, 2007, v. 4, p. 3176-3180 How to Cite?
AbstractSource camera identification is the process of discerning which camera has been used to capture a particular image. In our previous work, we tackled the problem with a vector of thirty-six features to train and test the classifier. The features include the lens aberration parameters and statistical measurements from pixel intensities. In this paper, we focus on reducing the feature set by stepwise discriminant analysis. Simulation is carried out to evaluate the classifier's performance by using the full feature set, reduced feature sets and randomly selected feature sets. The results show that the reduced feature sets can decrease the processing time while also maintain or even improve the classification accuracy under some circumstances. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/98901
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorKai, SCen_HK
dc.contributor.authorLam, EYen_HK
dc.contributor.authorWong, KKYen_HK
dc.date.accessioned2010-09-25T18:07:06Z-
dc.date.available2010-09-25T18:07:06Z-
dc.date.issued2007en_HK
dc.identifier.citationConference Proceedings - Ieee International Conference On Systems, Man And Cybernetics, 2007, v. 4, p. 3176-3180en_HK
dc.identifier.issn1062-922Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/98901-
dc.description.abstractSource camera identification is the process of discerning which camera has been used to capture a particular image. In our previous work, we tackled the problem with a vector of thirty-six features to train and test the classifier. The features include the lens aberration parameters and statistical measurements from pixel intensities. In this paper, we focus on reducing the feature set by stepwise discriminant analysis. Simulation is carried out to evaluate the classifier's performance by using the full feature set, reduced feature sets and randomly selected feature sets. The results show that the reduced feature sets can decrease the processing time while also maintain or even improve the classification accuracy under some circumstances. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofConference Proceedings - IEEE International Conference on Systems, Man and Cyberneticsen_HK
dc.titleFeature selection in source camera identificationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailLam, EY:elam@eee.hku.hken_HK
dc.identifier.emailWong, KKY:kywong@eee.hku.hken_HK
dc.identifier.authorityLam, EY=rp00131en_HK
dc.identifier.authorityWong, KKY=rp00189en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICSMC.2006.384605en_HK
dc.identifier.scopuseid_2-s2.0-34548142237en_HK
dc.identifier.hkuros116336en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548142237&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.spage3176en_HK
dc.identifier.epage3180en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridKai, SC=19640124000en_HK
dc.identifier.scopusauthoridLam, EY=7102890004en_HK
dc.identifier.scopusauthoridWong, KKY=36456599700en_HK

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