@InProceedings{BastosSchw:2017:AsReIm,
               author = "Bastos, Igor Leonardo Oliveira and Schwartz, William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais}",
                title = "Assigning Relative Importance to Scene Elements",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "human perception, context, importance of elements in a scene.",
             abstract = "The human brain is able to rapidly understand scenes through the 
                         recognition of their composing elements and comprehension of the 
                         role that each of them plays. This process, related to human 
                         perception, impacts in what people care when they see an image and 
                         the priority they give to each element. The idea of priority, also 
                         referred as importance, is based on biological features of 
                         perception and social aspects that interfere in how people 
                         perceive what they see and what is considered relevant. In this 
                         context, this paper proposes the Element Importance Relative 
                         Assignment (EIRA), an approach that models how humans attribute 
                         importance to elements in a scene. This approach is based on 
                         perceptual, compositional and contextual features employed to 
                         assign importance to elements in a scene. To evaluate the proposed 
                         approach, tests were conducted in different image datasets, with 
                         emphasis on the UIUC Pascal Sentence Dataset, where our approach 
                         achieved an average accuracy of 86.89%.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.61",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.61",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFH79E",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFH79E",
           targetfile = "PID4960099.pdf",
        urlaccessdate = "2024, Apr. 27"
}