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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/116222
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dc.contributor.authorMartins, G. B.-
dc.contributor.authorAfonso, L. C. S.-
dc.contributor.authorOsaku, D.-
dc.contributor.authorAlmeida, Jurandy-
dc.contributor.authorPapa, João Paulo-
dc.contributor.authorBayroCorrochano, E.-
dc.contributor.authorHancock, E.-
dc.date.accessioned2015-03-18T15:52:36Z-
dc.date.accessioned2016-10-25T20:23:38Z-
dc.date.available2015-03-18T15:52:36Z-
dc.date.available2016-10-25T20:23:38Z-
dc.date.issued2014-01-01-
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-12568-8_108-
dc.identifier.citationProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 893-900, 2014.-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/11449/116222-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/116222-
dc.description.abstractThis paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.en
dc.format.extent893-900-
dc.language.isoeng-
dc.publisherSpringer-
dc.sourceWeb of Science-
dc.subjectvideo summarizationen
dc.subjectoptimum-path foresten
dc.subjectclusteringen
dc.titleStatic Video Summarization through Optimum-Path Forest Clusteringen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationSao Paulo State Univ, UNESP, Dept Comp, BR-17033360 Bauru, SP, Brazil-
dc.description.affiliationUnespSao Paulo State Univ, UNESP, Dept Comp, BR-17033360 Bauru, SP, Brazil-
dc.identifier.doi10.1007/978-3-319-12568-8_108-
dc.identifier.wosWOS:000346407400108-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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