@InProceedings{AfonsoVidKurFalPap:2016:LeClSe,
               author = "Afonso, Luis Claudio Sugi and Vidal, Alexandre Campane and Kuroda, 
                         Michelle Chaves and Falcao, Alexandre Xavier and Papa, Joao 
                         Paulo",
          affiliation = "{Federal University of Sao Carlos} and {University of Campinas} 
                         and {University of Campinas} and {University of Campinas} and {Sao 
                         Paulo State University}",
                title = "Learning to Classify Seismic Images with Deep Optimum-Path 
                         Forest",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Optimum-Path Forest, Image Clustering, Deep Representations, 
                         Seismic Images.",
             abstract = "Due to the lack of labeled information, clustering techniques have 
                         been paramount in the last years once more. In this paper, 
                         inspired by the deep learning phenomenon, we presented a 
                         multi-scale approach to obtain more refined cluster 
                         representations of the Optimum-Path Forest (OPF) classifier, which 
                         has obtained promising results in a number of works in the 
                         literature. Here, we propose to fill a gap in OPF-based works by 
                         using a deep-driven representation of the feature space. 
                         Additionally, we validated the work in the context of high 
                         resolution seismic images aiming at petroleum exploration, as well 
                         as in general-purpose applications. Quantitative and qualitative 
                         analysis are conducted in order to assess the robustness of the 
                         proposed approach.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.062",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.062",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M3C9G8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M3C9G8",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 03"
}