Static Video Summarization through Optimum-Path Forest Clustering

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2015-03-18T15:52:36Z
dc.date.available2015-03-18T15:52:36Z
dc.date.created2015-03-18T15:52:36Z
dc.date.issued2014-01-01
dc.identifierProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 893-900, 2014.
dc.identifier0302-9743
dc.identifierhttp://hdl.handle.net/11449/116222
dc.identifier10.1007/978-3-319-12568-8_108
dc.identifierWOS:000346407400108
dc.identifier9039182932747194
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.
dc.languageeng
dc.publisherSpringer
dc.relationProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014
dc.relation0,295
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectvideo summarization
dc.subjectoptimum-path forest
dc.subjectclustering
dc.titleStatic Video Summarization through Optimum-Path Forest Clustering
dc.typeActas de congresos


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