@InProceedings{ValemPedr:2016:UnSiLe,
               author = "Valem, Lucas Pascotti and Pedronette, Daniel Carlos 
                         Guimar{\~a}es",
                title = "Unsupervised Similarity Learning through Cartesian Product of 
                         Ranking References for Image Retrieval Tasks",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "content-based image retrieval, unsupervised learning, Cartesian 
                         product, effectiveness, efficiency.",
             abstract = "Despite the consistent advances in visual features and other 
                         Content-Based Image Retrieval techniques, measuring the similarity 
                         among images is still a challenging task for effective image 
                         retrieval. In this scenario, similarity learning approaches 
                         capable of improving the effectiveness of retrieval in an 
                         unsupervised way are indispensable. A novel method, called 
                         Cartesian Product of Ranking References (CPRR), is proposed with 
                         this objective in this paper. The proposed method uses Cartesian 
                         product operations based on rank information for exploiting the 
                         underlying structure of datasets. Only subsets of ranked lists are 
                         required, demanding low computational efforts. An extensive 
                         experimental evaluation was conducted considering various aspects, 
                         four public datasets and several image features. Besides 
                         effectiveness, experiments were also conducted to assess the 
                         efficiency of the method, considering parallel and heterogeneous 
                         computing on CPU and GPU devices. The proposed method achieved 
                         significant effectiveness gains, including competitive 
                         state-of-the-art results on popular benchmarks.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.042",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.042",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5J46P",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5J46P",
           targetfile = "PaperSIBGRAPI-2016_vFinal.pdf",
        urlaccessdate = "2024, May 03"
}