author = "Brandoli, Bruno and Eler, Danilo Medeiros and Paulovich, Fernando 
                         and Minghim, Rosane and ESB Neto, Jo{\~a}o do",
          affiliation = "{ICMC - University of S{\~a}o Paulo} and {ICMC - University of 
                         S{\~a}o Paulo} and {ICMC - University of S{\~a}o Paulo} and 
                         {ICMC - University of S{\~a}o Paulo} and {ICMC - University of 
                         S{\~a}o Paulo}",
                title = "Visual Data Exploration to Feature Space Definition",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "isual Feature Space Analysis, Feature Space Visualization, Feature 
                         Space Evaluation, Visual Exploration.",
             abstract = "ABSTRACT: Many image-related applications rely on the fact that 
                         the dataset under investigation is correctly represented through 
                         features. However, defining a set of features that properly 
                         represents a dataset is still a challenging and, in most cases, an 
                         exhausting task. Most of the available techniques, especially when 
                         a large number of features is considered, are based on purely 
                         quantitative statistical measures or approaches based on 
                         artificial intelligence, and normally are {"}black-boxes{"} to the 
                         user. The approach proposed here seeks to open this 
                         {"}black-box{"} by means of visual representations, enabling users 
                         to get insight about the meaning and representativeness of the 
                         features computed from different feature extraction algorithms and 
                         sets of parameters. The results show that, as the combination of 
                         sets of features and changes in parameters improves the quality of 
                         the visual representation, the accuracy of the classification 
                         using the defined set of features also improves. The results 
                         strongly suggest that our approach can be successfully employed as 
                         a guidance to defining and understanding a set of features that 
                         properly represents an image dataset.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "70614_2.pdf",
        urlaccessdate = "2020, Nov. 28"