author = "Pimentel Filho, Carlos Alberto Fraga and Ara{\'u}jo, Arnaldo de 
                         Albuquerque and Crucianu, Michel and Gouet-Brunet, Val{\'e}rie",
          affiliation = "{Federal University of Minas Gerais} and {Federal University of 
                         Minas Gerais} and {Conservatoire National des Arts et M{\'e}tiers 
                         - CEDRIC} and {IGN - Laboratoire MATIS}",
                title = "Sketch-Finder: efficient and effective sketch-based retrieval for 
                         large image collections",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "sketch-based image retrieval, multimedia indexing, scalability.",
             abstract = "Among various image retrieval approaches, the use of sketches lets 
                         one express a precise visual query with simple and widespread 
                         means. The challenge consists in finding a content representation 
                         that allows you to effectively compare sketches and images, while 
                         supporting efficient retrieval in order to make the system 
                         scalable. We put forward a sketch-based image retrieval solution 
                         where sketches and natural image contours are represented and 
                         compared in the wavelet domain. The relevant information regarding 
                         query sketches and image content has, thus, a compact 
                         representation that can be readily employed by an efficient index 
                         for retrieval by similarity. Furthermore, with this solution, the 
                         balance between effectiveness and efficiency can be easily 
                         modified in order to adapt to the available resources. A 
                         comparative evaluation with a state-of-the-art method on the Paris 
                         dataset and a subset with 535K images of the ImageNet dataset 
                         shows that our solution can preserve effectiveness while being 
                         more than one order of magnitude faster.",
  conference-location = "Arequipa, Peru",
      conference-year = "Aug. 5-8, 2013",
             language = "en",
           targetfile = "PID2854709.pdf",
        urlaccessdate = "2020, Nov. 25"