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		<identifier>8JMKD3MGPAW/3PJ69JP</identifier>
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		<citationkey>MartinsAlmePapa:2017:ViPrAn</citationkey>
		<title>Video processing and analysis through Optimum-Path Forest</title>
		<format>On-line</format>
		<year>2017</year>
		<date>Oct. 17-20, 2017</date>
		<numberoffiles>1</numberoffiles>
		<size>226 KiB</size>
		<author>Martins, Guilherme Brandão,</author>
		<author>Almeida, Jurandy,</author>
		<author>Papa, João Paulo,</author>
		<affiliation>São Paulo State University</affiliation>
		<affiliation>Federal University of São Paulo</affiliation>
		<affiliation>São Paulo State University</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>gui.bmartins3@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>video summarization, clustering, optimum-path forest.</keywords>
		<abstract>Currently, a number of improvements related to computational networks and data storage technologies have allowed a considerable amount of digital content to be provided on the Internet, mainly through social networks. In order to exploit this context, video processing and pattern recognition approaches have received a considerable attention in the last years. The main goal of this work is to employ the OptimumPath Forest classifier in both video summarization and video genre classification processes as well as to conduct a viability study of such classifier in the aforementioned contexts. The resultshave shown this classifier can achieve promising performances, being very close in terms of summary quality and consistent recognition rates to some state-of-the-art video summarization and video genre classification approaches, respectively.</abstract>
		<language>en</language>
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