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dc.contributor.authorSaraç, Mustafa İlkeren_US
dc.contributor.authorDuygulu Pınaren_US
dc.coverage.spatialBarcelona, Spain
dc.date.accessioned2016-02-08T12:03:11Z
dc.date.available2016-02-08T12:03:11Z
dc.date.issued2014-10en_US
dc.identifier.urihttp://hdl.handle.net/11693/27859
dc.descriptionDate of Conference: 16-17 October, 2014
dc.descriptionConference name: MediaEval 2014 Workshop
dc.description.abstractThis paper explains the approach proposed by Bilkent - RETINA team for the Retrieving Diverse Social Images task of MediaEval 2014 [1]. We develop a framework which rst removes outliers using one-class support vector machines (SVM) to improve relevance. Second it clusters the eliminated set and retrieves the centroids to diversify the results. We tried to exploit visual only features during our experiments. For the rst run we used the provided visual features and for the second run we used well known visual features like SIFT [2] and GIST [4].en_US
dc.language.isoEnglishen_US
dc.source.titleCEUR Workshop Proceedingsen_US
dc.subjectOne-class support vector machineen_US
dc.subjectSocial imagesen_US
dc.subjectVisual featureen_US
dc.subjectSupport vector machinesen_US
dc.titleBilkent-RETINA at retrieving diverse social images task of MediaEval 2014en_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.publisherCEUR-WSen_US


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