@InProceedings{RibeiroTeiFerJrNas:2016:CoAsLa,
               author = "Ribeiro, Manoel Horta and Teixeira, Bruno and Fernandes, 
                         Ant{\^o}nio Ot{\'a}vio and Jr. , Wagner Meira and Nascimento, 
                         Erickson R.",
          affiliation = "Computer Science Department, Universidade Federal de Minas Gerais 
                         and Computer Science Department, Universidade Federal de Minas 
                         Gerais and Computer Science Department, Universidade Federal de 
                         Minas Gerais and Computer Science Department, Universidade Federal 
                         de Minas Gerais and Computer Science Department, Universidade 
                         Federal de Minas Gerais",
                title = "Complexity-Aware Assignment of Latent Values in Discriminative 
                         Models for Accurate Gesture Recognition",
            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 = "discriminative models, conditional random fields, gesture 
                         recognition, activity recognition.",
             abstract = "Many of the state-of-the-art algorithms for gesture recognition 
                         are based on Conditional Random Fields (CRFs). Successful 
                         approaches, such as the Latent-Dynamic CRFs, extend the CRF by 
                         incorporating latent variables, whose values are mapped to the 
                         values of the labels. In this paper we propose a novel methodology 
                         to set the latent values according to the gesture complexity. We 
                         use an heuristic that iterates through the samples associated with 
                         each label value, estimating their complexity. We then use it to 
                         assign the latent values to the label values. We evaluate our 
                         method on the task of recognizing human gestures from video 
                         streams. The experiments were performed in binary datasets, 
                         generated by grouping different labels. Our results demonstrate 
                         that our approach outperforms the arbitrary one in many cases, 
                         increasing the accuracy by up to 10\%.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.059",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.059",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M4UNG2",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M4UNG2",
           targetfile = "manoel_sibgrapi_final_version.pdf",
        urlaccessdate = "2024, May 03"
}