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@InProceedings{CámaraChávezCorPrePhiAlb:2006:ViSeSu,
               author = "C{\'a}mara Ch{\'a}vez, Guillermo and Cord, Matthieu and 
                         Precioso, Frederic and Philipp-Foliguet, Sylvie and de Albuquerque 
                         Ara{\'u}jo, Arnaldo",
          affiliation = "{Equipe Traiment des Images et du Signal - ENSEA} and {Equipe 
                         Traiment des Images et du Signal - ENSEA} and {Equipe Traiment des 
                         Images et du Signal - ENSEA} and {Equipe Traiment des Images et du 
                         Signal - ENSEA} and {Departamento de Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o - UFMG}",
                title = "Video Segmentation by Supervised Learning",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "video segmentation, cut detection, supervised learning.",
             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.",
  conference-location = "Manaus",
      conference-year = "8-11 Oct. 2006",
             language = "en",
           targetfile = "sibgrapi_camara_video.pdf",
        urlaccessdate = "2020, Nov. 27"
}


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