@InProceedings{BelémJoćoGuimFalc:2019:ImObSe,
               author = "Bel{\'e}m, Felipe de Castro and Jo{\~a}o, Leonardo de Melo and 
                         Guimar{\~a}es, Silvio Jamil Ferzoli and Falc{\~a}o, Alexandre 
                         Xavier",
          affiliation = "{University of Campinas} and {University of Campinas} and 
                         {Pontifical Catholic University of Minas Gerais} and {University 
                         of Campinas}",
                title = "The Importance of Object-based Seed Sampling for Superpixel 
                         Segmentation",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "superpixels, IFT, segmentation, object saliency map.",
             abstract = "Superpixel segmentation can be defined as an image partition into 
                         connected regions, such that image objects may be represented by 
                         the union of their superpixels.In this context, multiple 
                         iterations of superpixel segmentation from improved seed sets is a 
                         strategy exploited by several algorithms. The Iterative Spanning 
                         Forest (ISF) framework divides this strategy into three 
                         independent components: a seed sampling method, a superpixel 
                         delineation algorithm based on strength of connectedness between 
                         seeds and pixels, and a seed recomputation procedure. A recent 
                         work shows that object information can be added to each component 
                         of ISF such that the user can control the number of seeds inside 
                         the objects and so improve superpixel segmentation. However, it is 
                         uncertain how the added information impacts each component of the 
                         pipeline. Therefore, in this work, a study is conducted to 
                         evaluate such inclusion in the seed sampling procedure, partially 
                         elucidating its benefits. Additionally, we introduce a novel 
                         object-based sampling approach, named Object Saliency Map sampling 
                         by Ordered Extraction (OSMOX), and demonstrate the results for 
                         supervised and unsupervised object information. The experiments 
                         show considerable improvements in under-segmentation error, 
                         specially with a low number of superpixels.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00023",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00023",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U344R5",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U344R5",
           targetfile = "SIBGRAPI_2019.pdf",
        urlaccessdate = "2024, Apr. 27"
}