Static video summarization through Optimum-Path Forest clustering

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2018-12-11T16:40:07Z
dc.date.available2018-12-11T16:40:07Z
dc.date.created2018-12-11T16:40:07Z
dc.date.issued2014-01-01
dc.identifierLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 893-900.
dc.identifier1611-3349
dc.identifier0302-9743
dc.identifierhttp://hdl.handle.net/11449/168181
dc.identifier2-s2.0-84949130645
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.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation0,295
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectClustering
dc.subjectOptimum-path forest
dc.subjectVideo summarization
dc.titleStatic video summarization through Optimum-Path Forest clustering
dc.typeActas de congresos


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