author = "Fabian, Junior and Pires, Ramon and Rocha, Anderson",
          affiliation = "{University of Campinas (Unicamp)} and {University of Campinas 
                         (Unicamp)} and {University of Campinas (Unicamp)}",
                title = "Searching for People through Textual and Visual Attributes",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno, 
                         Roberto and Silva, Luciano",
         organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Face Search, Rank Fusion, Visual Dictionaries.",
             abstract = "Searching for people through their personal traits has been 
                         largely required for several areas and, consequently, has become 
                         the center of attention in the scientific community. Locating a 
                         suspect or finding missing people in a public space are some of 
                         the practical applications which take advantage of research 
                         conducted in this topic. In this paper, we propose the use of 
                         describable visual attributes (e.g, male, wear glasses, has 
                         beard), as labels that can be assigned to an image to describe its 
                         appearance. The approach is based on visual dictionaries to 
                         generate an intermediate representation for the face images. We 
                         train binary classifiers for the attributes which give to each 
                         image a score used to obtain its ranking. However, there are some 
                         attributes that have no immediate antagonistic (e.g., asian 
                         people). Then, we evaluate unary classifiers for such attributes. 
                         The method is easily extensible to new attributes. For queries 
                         consisting of more than one attribute, we use two approaches of 
                         the state-of-the-art to combine the rankings: product of 
                         probabilities and rank aggregation. Experimental results show that 
                         incorporating visual dictionaries improves the accuracy for some 
                         attributes. Furthermore, for many attributes, rank aggregation 
                         achieves better results than traditional methods of rank fusion. 
                         The proposed solution might be of interest in a forensic scenario 
                         for searching suspects in a database by means of textual 
                         descriptions provided by a victim.",
  conference-location = "Ouro Preto",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "Searching for People through Textual and Visual Attributes.pdf",
        urlaccessdate = "2021, Jan. 27"