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DC poleHodnotaJazyk
dc.contributor.authorAzam, Samiul
dc.contributor.authorGavrilova, Marina
dc.contributor.editorSkala, Václav
dc.date.accessioned2016-07-25T12:54:00Z
dc.date.available2016-07-25T12:54:00Z
dc.date.issued2016
dc.identifier.citationJournal of WSCG. 2016, vol. 24, no. 2, p. 53-62.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2016/!_2016_Journal_WSCG-No-2.pdf
dc.identifier.urihttp://hdl.handle.net/11025/21646
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectvlastnostics
dc.subjectvnímánícs
dc.subjectobrazcs
dc.subjectpohlavícs
dc.subjectgenetický algoritmuscs
dc.titleGender prediction using individual perceptual image aestheticsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedImages have rarely been used for psychological behavior analysis or for person identification in the information technology domain of research. In this paper, we present one of the first methods that allows to accurately predict gender from a collection of person’s favorite images. We select 56 image aesthetic features, and propose a mixture of expert models consisting of support vector machine, K-nearest neighbor and Decision tree. Final decision is taken based on the weighted combination of probability generated by individual classifiers. We introduce a genetic algorithm based method to improve the prediction accuracy of the model, which allows us to find the best combination of feature subset in 56D binary search space. Moreover, feature dimension is reduced significantly that decreases the testing time. Finally, three weights of the prediction model are adjusted using genetic algorithm in 3D real-number search space. Experimental results conducted on a true image database of 24000 images provided by 120 Flickr users. The experimental results demonstrate superiority of the proposed method over other approaches for gender prediction from perceptual image aesthetics preferences.en
dc.subject.translatedfeaturesen
dc.subject.translatedperceptionen
dc.subject.translatedimageen
dc.subject.translatedgenderen
dc.subject.translatedgenetic algorithmen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 24, Number 2 (2016)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 24 (2016)
  6. Volume 24, Number 2 (2016)