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@InProceedings{AlmeidaJrGuim:2018:CaStHu,
               author = "Almeida, Raquel and Jr. , Zenilton K. G. do Pratroc{\'{\i}}nio 
                         and Guimar{\~a}es, Silvio Jamil F.",
          affiliation = "{Pontifical Catholic University of Minas Gerais} and {Pontifical 
                         Catholic University of Minas Gerais} and {Pontifical Catholic 
                         University of Minas Gerais}",
                title = "Exploring Feature Distribution to Create Mid-level 
                         Representations: A Case Study in Human Action Recognition",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "data representation, mid-level, bag-of-words, human action 
                         recognition.",
             abstract = "Data representation is a critical task in many areas of 
                         computational studies, particularly in the case of visual data 
                         representation, in which subtleties can undermine the perception 
                         and interpretation of the visual content. In this study, it is 
                         proposed strategies to exploit visual mid-level representations, 
                         aiming to transform the detailed description extracted directly 
                         from the visual media into a simplified and discriminative 
                         representation. More specifically, the proposed strategies are 
                         delineated in Bag-of-Words mid-level representation model and are 
                         used to aggregate distribution information within partitions and 
                         regions of interest on feature space. Experiments on three 
                         well-known public datasets, namely, KTH, UCF Sports and UCF 11, 
                         demonstrated that feature points spatial distribution information 
                         is useful to create more discriminative representations. All three 
                         proposed representations were published and outperform, in terms 
                         of recognition rate, conventional strategies on BoW model and are, 
                         in many cases, superior or comparable with the state-of-the-art.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S3HG65",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S3HG65",
           targetfile = "paperID5.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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