@InProceedings{SchwartzDavi:2009:LeDiAp,
               author = "Schwartz, William Robson and Davis, Larry S.",
          affiliation = "{University of Maryland} and {University of Maryland}",
                title = "Learning Discriminative Appearance-Based Models Using Partial 
                         Least Squares",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Partial least squares, PLS, appearance-based recognition, 
                         co-occurrence matrix, HOG.",
             abstract = "Appearance information is essential for applications such as 
                         tracking and people recognition. One of the main problems of using 
                         appearance-based discriminative models is the ambiguities among 
                         classes when the number of persons being considered increases. To 
                         reduce the amount of ambiguity, we propose the use of a rich set 
                         of feature descriptors based on color, textures and edges. Another 
                         issue regarding appearance modeling is the limited number of 
                         training samples available for each appearance. The discriminative 
                         models are created using a powerful statistical tool called 
                         Partial Least Squares (PLS), responsible for weighting the 
                         features according to their discriminative power for each 
                         different appearance. The experimental results, based on 
                         appearance-based person recognition, demonstrate that the use of 
                         an enriched feature set analyzed by PLS reduces the ambiguity 
                         among different appearances and provides higher recognition rates 
                         when compared to other machine learning techniques.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.42",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.42",
             language = "en",
                  ibi = "8JMKD3MGPBW4/363S8PB",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/363S8PB",
           targetfile = "paper_CameraReady.pdf",
        urlaccessdate = "2024, May 02"
}