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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.22.22.38
%2 sid.inpe.br/sibgrapi/2018/10.22.22.38.53
%T Classificação de texturas usando a métrica de centralidade closeness
%D 2018
%A Magalhães, Whendell Feijó,
%A Queiroz, Fabiane da Silva,
%A Cabral, Raquel da Silva,
%@affiliation Universidade Federal de Alagoas - Campus Arapiraca
%@affiliation Universidade Federal de Alagoas - Centro de Ciências Agrárias
%@affiliation Universidade Federal de Alagoas - Campus Arapiraca
%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,
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%C Foz do Iguaçu, PR, Brazil
%8 Oct. 29 - Nov. 1, 2018
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K redes complexas, classificação de texturas, centralidade closeness.
%X In this paper, we propose a method for automatic description and classification of image texture. The images are modeled as weighted directed graphs. We use the centrality measure closeness and in-degree to generate a feature vector that describes the texture information. To validate the method, we train a k- Nearest Neighbors classifier and compare the obtained results with the Co-occurrence Matrix and Local Binary Patterns texture description techniques. For the experiments, we use the public dataset, KTH-TIPS. The accuracy of the proposed method is 95,52% that overcome the compared techniques.
%@language pt
%3 Classificação de Texturas Usando a Métrica de Centralidade Closeness.pdf


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