@InProceedings{LopesAvPeOlCoAr:2009:NuDeVi,
               author = "Lopes, Ana Paula Brand{\~a}o and Avila, Sandra Eliza Fonte de and 
                         Peixoto, Anderson Nunes Alves and Oliveira, Rodrigo Silva and 
                         Coelho, Marcelo de Miranda and Ara{\'u}jo, Arnaldo de 
                         Albuquerque",
          affiliation = "Federal University of Minas Gerais (UFMG), State University of 
                         Santa Cruz (UESC) and {Federal University of Minas Gerais (UFMG)} 
                         and {Federal University of Minas Gerais (UFMG)} and {Federal 
                         University of Minas Gerais (UFMG)} and Federal University of Minas 
                         Gerais (UFMG), Preparatory School of Air Cadets (EPCAR) and 
                         {Federal University of Minas Gerais (UFMG)}",
                title = "Nude detection in video using bag-of-visual-features",
            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 = "nude detection, bag-of-visual-features, video classification.",
             abstract = "The ability to filter improper content from multimedia sources 
                         based on visual content has important applications, since 
                         text-based filters are clearly insufficient against erroneous 
                         and/or malicious associations between text and actual content. In 
                         this paper, we investigate a method for detection of nudity in 
                         videos based on a bag-of-visual-features representation for frames 
                         and an associated voting scheme. Bag-of-Visual-Features (BoVF) 
                         approaches have been successfully applied to object recognition 
                         and scene classification, showing robustness to occlusion and also 
                         to the several kinds of variations that normally curse object 
                         detection methods. To the best of our knowledge, only two 
                         proposals in the literature use BoVF for nude detection in still 
                         images, and no other attempt has been made at applying BoVF for 
                         videos. Nevertheless, the results of our experiments show that 
                         this approach is indeed able to provide good recognition rates for 
                         nudity even at the frame level and with a relatively low sampling 
                         ratio. Also, the proposed voting scheme significantly enhances the 
                         recognition rates for video segments, achieving, in the best case, 
                         a value of 93.2% of correct classification, using a sampling ratio 
                         of 1/15 frames. Finally, a visual analysis of some particular 
                         cases indicates possible sources of misclassifications.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.32",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.32",
             language = "en",
                  ibi = "8JMKD3MGPBW4/35THDDS",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/35THDDS",
           targetfile = "PID949976.pdf",
        urlaccessdate = "2024, May 02"
}