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		<identifier>8JMKD3MGPAW/3S49SBP</identifier>
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		<citationkey>MagalhãesQueiCabr:2018:ClTeUs</citationkey>
		<title>Classificação de texturas usando a métrica de centralidade closeness</title>
		<format>On-line</format>
		<year>2018</year>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<numberoffiles>2</numberoffiles>
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		<author>Magalhães, Whendell Feijó,</author>
		<author>Queiroz, Fabiane da Silva,</author>
		<author>Cabral, Raquel da Silva,</author>
		<affiliation>Universidade Federal de Alagoas - Campus Arapiraca</affiliation>
		<affiliation>Universidade Federal de Alagoas - Centro de Ciências Agrárias</affiliation>
		<affiliation>Universidade Federal de Alagoas - Campus Arapiraca</affiliation>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<e-mailaddress>whendell.magalhaes@arapiraca.ufal.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<keywords>redes complexas, classificação de texturas, centralidade closeness.</keywords>
		<abstract>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.</abstract>
		<language>pt</language>
		<targetfile>Classificação de Texturas Usando a Métrica de Centralidade Closeness.pdf</targetfile>
		<usergroup>whendell.magalhaes@arapiraca.ufal.br</usergroup>
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