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		<citationkey>CámaraChávezCorPrePhiAlb:2006:ViSeSu</citationkey>
		<title>Video Segmentation by Supervised Learning</title>
		<format>On-line</format>
		<year>2006</year>
		<date>8-11 Oct. 2006</date>
		<numberoffiles>1</numberoffiles>
		<size>208 KiB</size>
		<author>Cámara Chávez, Guillermo,</author>
		<author>Cord, Matthieu,</author>
		<author>Precioso, Frederic,</author>
		<author>Philipp-Foliguet, Sylvie,</author>
		<author>de Albuquerque Araújo, Arnaldo,</author>
		<affiliation>Equipe Traiment des Images et du Signal - ENSEA</affiliation>
		<affiliation>Equipe Traiment des Images et du Signal - ENSEA</affiliation>
		<affiliation>Equipe Traiment des Images et du Signal - ENSEA</affiliation>
		<affiliation>Equipe Traiment des Images et du Signal - ENSEA</affiliation>
		<affiliation>Departamento de Ciência da Computação - UFMG</affiliation>
		<editor>Oliveira Neto, Manuel Menezes de,</editor>
		<editor>Carceroni, Rodrigo Lima,</editor>
		<e-mailaddress>chavez@ensea.fr</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)</conferencename>
		<conferencelocation>Manaus</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>video segmentation, cut detection, supervised learning.</keywords>
		<abstract>In most of video shot boundary detection algorithms, proposed in the literature, several parameters and thresholds have to be set in order to achieve good results. In this paper, to get rid of parameters and thresholds, we explore a supervised  classification method for video shot segmentation. We transform the temporal segmentation into a class categorization issue. Our approach defines a uniform framework for combining different kinds of features extracted from the video. Our method does not require any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. The experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classification method.</abstract>
		<language>en</language>
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