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@InProceedings{MartinsAlmePapa:2017:ViPrAn,
               author = "Martins, Guilherme Brand{\~a}o and Almeida, Jurandy and Papa, 
                         Jo{\~a}o Paulo",
          affiliation = "{S{\~a}o Paulo State University} and {Federal University of 
                         S{\~a}o Paulo} and {S{\~a}o Paulo State University}",
                title = "Video processing and analysis through Optimum-Path Forest",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "video summarization, clustering, optimum-path forest.",
             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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJ69JP",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJ69JP",
           targetfile = "Paper_Martins_SIBGRAPI2017_WTD.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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