author = "Galv{\~a}o, Felipe Lemes and Falc{\~a}o, Alexandre Xavier and 
                         Chowdhury, Ananda Shankar",
          affiliation = "IC-Unicamp and IC-Unicamp and {Jadavpur University}",
                title = "RISF: Recursive Iterative Spanning Forest for superpixel 
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image Foresting Transform, image segmentation, superpixel 
             abstract = "Methods for superpixel segmentation have become very popular in 
                         computer vision. Recently, a graph-based framework named ISF 
                         (Iterative Spanning Forest) was proposed to obtain connected 
                         superpixels (supervoxels in 3D) based on multiple executions of 
                         the Image Foresting Transform (IFT) algorithm from a given choice 
                         of four components: a seed sampling strategy, an adjacency 
                         relation, a connectivity function, and a seed recomputation 
                         procedure. In this paper, we extend ISF to introduce a unique 
                         characteristic among superpixel segmentation methods. Using the 
                         new framework, termed as Recursive Iterative Spanning Forest 
                         (RISF), one can recursively generate multiple segmentation scales 
                         on region adjacency graphs (i.e., a hierarchy of superpixels) 
                         without sacrificing the efficiency and effectiveness of ISF. In 
                         addition to a hierarchical segmentation, RISF allows a more 
                         effective geodesic seed sampling strategy, with no negative impact 
                         in the efficiency of the method. For a fixed number of scales 
                         using 2D and 3D image datasets, we show that RISF can consistently 
                         outperform the most competitive ISF-based methods.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "89.pdf",
        urlaccessdate = "2020, Dec. 02"