An analytic distance metric for Gaussian mixture models with application in image retrieval

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dc.contributor.authorSfikas, G.en
dc.contributor.authorConstantinopoulos, C.en
dc.contributor.authorLikas, A.en
dc.contributor.authorGalatsanos, N. P.en
dc.date.accessioned2015-11-24T17:01:08Z
dc.date.available2015-11-24T17:01:08Z
dc.identifier.issn0302-9743-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10875
dc.rightsDefault Licence-
dc.titleAn analytic distance metric for Gaussian mixture models with application in image retrievalen
heal.abstractIn this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL) divergence it can be computed in closed form when the PDFs are modeled as Gaussian Mixtures (GM). The application in mind for this metric is histogram based image retrieval. We experimentally show that in an image retrieval scenario the proposed metric provides as good results as the KL divergence at a fraction of the computational cost. This metric is also compared to a Bhattacharyya-based distance metric that can be computed in closed form for GMs and is found to produce better results.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.journalNameArtificial Neural Networks: Formal Models and Their Applications - Icann 2005, Pt 2, Proceedingsen
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2005-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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