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@InProceedings{PereiraSant:2017:ImReLe,
               author = "Pereira, {\'E}rico Marco Dias Alves and dos Santos, Jefersson 
                         Alex",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais}",
                title = "Image representation learning by color quantization optimization",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "representation learning, color quantization, CBIR, genetic 
                         algorithm, feature extraction.",
             abstract = "The state-of-art methods of representation learning, based on Deep 
                         Neural Networks, present serious drawbacks regarding usage 
                         complexity and resources consumption, leaving space for simpler 
                         alternatives. We proposed two approaches of a Representation 
                         Learning method which aims to provide more effective and compact 
                         image representations by optimizing the colour quantization for 
                         the image domain. Our hypothesis is that changes in the 
                         quantization affect the description quality of the features 
                         enabling representation improvements. We evaluated the method 
                         performing experiments for the task of Content-Based Image 
                         Retrieval on eight known datasets. The results showed that the 
                         first approach, focused on representation effectiveness, produced 
                         representations that outperforms the baseline in all the tested 
                         scenarios. And the second, focused on compactness, was able to 
                         produce superior results maintaining or even reducing the 
                         dimensionality and representations until 25% smaller that 
                         presented statistically equivalent performance.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJ6MCH",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJ6MCH",
           targetfile = "Pereira_DosSantos_2017.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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