author = "Cavalin, Paulo and Oliveira, Luiz S.",
          affiliation = "{IBM Research} and {Universidade Federal do Paran{\'a} - UFPR}",
                title = "A Review of Texture Classification Methods and Databases",
            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 = "Texture recognition, Image recognition, Deep Learn- ing.",
             abstract = "In this survey, we present a review of methods and resources for 
                         texture recognition, presenting the most common techniques that 
                         have been used in the recent decades, along with current 
                         tendencies. That said, this paper covers since the most 
                         traditional approaches, for instance texture descriptors such as 
                         gray-level co-occurence matrices (GLCM) and Local Binary Patterns 
                         (LBP), to more recent approaches such as Convolutional Neural 
                         Networks (CNN) and multi-scale patch-based recognition based on 
                         encoding approaches such as Fisher Vectors. In addition, we point 
                         out relevant references for benchmark datasets, which can help the 
                         reader develop and evaluate new methods.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJSQNL",
                  url = "",
           targetfile = "sibgrapi_paper2017.pdf",
        urlaccessdate = "2021, Mar. 02"