author = "Cuadros, Oscar and Botelho, Glenda Michele and Rodrigues, 
                         Francisco and Batista Neto, Jo{\~a}o do Esp{\'{\i}}rito Santo",
          affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade de S{\~a}o 
                         Paulo} and {Universidade de S{\~a}o Paulo} and {Universidade de 
                         S{\~a}o Paulo}",
                title = "Segmentation of Large Images with Complex Networks",
            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 = "Image segmentation, complex networks and super pixels.",
             abstract = "Image segmentation is still a challenging issue in pattern 
                         recognition. Among the various segmentation approaches are those 
                         based on graph partitioning, which present some drawbacks, one 
                         being high processing times. With the recent developments on 
                         complex networks theory, pattern recognition techniques based on 
                         graphs have improved considerably. The identification of cluster 
                         of vertices can be considered a process of community 
                         identification according to complex networks theory. Since data 
                         clustering is related with image segmentation, image segmentation 
                         can also be approached via complex networks. However, image 
                         segmentation based on complex networks poses a fundamental 
                         limitation which is the excessive numbers of nodes in the network. 
                         This paper presents a complex network approach for large image 
                         segmentation that is both accurate and fast. To that, we 
                         incorporate the concept of super pixels, to reduce the number of 
                         nodes in the network. We evaluate our method for both synthetic 
                         and real images. Results show that our method can outperform other 
                         graph-based methods both in accuracy and processing times.",
  conference-location = "Ouro Preto",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "Paper.pdf",
        urlaccessdate = "2021, Jan. 28"