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@InProceedings{FreitasMira:2018:NeShDe,
               author = "Freitas, Anderson M. and Miranda, Paulo A. V.",
          affiliation = "Institute of Mathematics and Statistics, University of S{\~a}o 
                         Paulo and Institute of Mathematics and Statistics, University of 
                         S{\~a}o Paulo",
                title = "New Shape Descriptors based on Tensor Scale with Global Features",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Tensor Scale, Shape Descriptors, Content-Based Image Retrieval.",
             abstract = "In this work, two new shape descriptors are proposed for tasks in 
                         Content-Based Image Retrieval (CBIR) and Shape Analysis, which are 
                         built upon an extended tensor scale based on the Euclidean 
                         Distance Transform (EDT). First, the tensor scale algorithm is 
                         applied to extract shape attributes from its local structures as 
                         represented by the largest ellipse within a homogeneous region 
                         centered at each image pixel. In the new descriptors, the upper 
                         limit of the interval of local orientation of tensor scale 
                         ellipses is extended from \π to 2\π, to discriminate 
                         the description of local structures better. Then, the new 
                         descriptors are built based on different sampling approaches, 
                         aiming to summarize the most relevant features. Experimental 
                         results for different shape datasets (MPEG-7 and MNIST) are 
                         presented to illustrate and validate the methods. TSS can achieve 
                         high retrieval values comparable to state-of-the-art methods, 
                         which usually rely on time-consuming correspondence optimization 
                         algorithms, but uses a more straightforward and faster distance 
                         function, while the even faster linear complexity of TSB leads to 
                         a suitable solution for huge shape collections.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S4NEGH",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S4NEGH",
           targetfile = "tss-tsb-descriptors-sibgrapi-wtd-2018 (1).pdf",
        urlaccessdate = "2020, Nov. 29"
}


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