@InProceedings{CamposMantJr:2016:MeApRe,
               author = "Campos, Gabriel F. C. and Mantovani, Rafael G. and Jr., Sylvio 
                         Barbon",
          affiliation = "{Londrina State University (UEL)} and {University of Sao Paulo 
                         (USP)} and {Londrina State University (UEL)}",
                title = "A Meta-learning Approach for Recommendation of Image Segmentation 
                         Algorithms",
            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 = "Segmentation algorithm recommendation, metalearning, image 
                         processing.",
             abstract = "There are many algorithms for image segmentation, but there is no 
                         optimal algorithm for all kind of image applications. To recommend 
                         an adequate algorithm for segmentation is a challenging task that 
                         requires knowledge about the problem and algorithms. Meta-learning 
                         has recently emerged from machine learning research field to solve 
                         the algorithm selection problem. This paper applies meta-learning 
                         to recommend segmentation algorithms based on meta-knowledge. We 
                         performed experiments in four different meta-databases 
                         representing various real world problems, recommending when three 
                         different segmentation techniques are adequate or not. A set of 44 
                         features based on color, frequency domain, histogram, texture, 
                         contrast and image quality were extracted from images, obtaining 
                         enough discriminative power for the recommending task in different 
                         segmentation scenarios. Results show that Random Forest 
                         meta-models were able to recommend segmentation algorithms with 
                         high predictive performance.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.058",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.058",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M3PPPL",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M3PPPL",
           targetfile = "PID4348117.pdf",
        urlaccessdate = "2024, May 02"
}