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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.25.19.19
%2 sid.inpe.br/sibgrapi/2018/10.25.19.19.49
%A Freitas, Anderson M.,
%A Miranda, Paulo A. V.,
%@affiliation Institute of Mathematics and Statistics, University of São Paulo
%@affiliation Institute of Mathematics and Statistics, University of São Paulo
%T New Shape Descriptors based on Tensor Scale with Global Features
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%D 2018
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%S Proceedings
%8 Oct. 29 - Nov. 1, 2018
%J Porto Alegre
%I Sociedade Brasileira de Computação
%C Foz do Iguaçu, PR, Brazil
%K Tensor Scale, Shape Descriptors, Content-Based Image Retrieval.
%X 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 2π, 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.
%@language en
%3 tss-tsb-descriptors-sibgrapi-wtd-2018 (1).pdf


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