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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3S49SBP
Repositorysid.inpe.br/sibgrapi/2018/10.22.22.38
Last Update2018:10.22.22.49.40 whendell.magalhaes@arapiraca.ufal.br
Metadatasid.inpe.br/sibgrapi/2018/10.22.22.38.53
Metadata Last Update2020:02.20.22.06.51 administrator
Citation KeyMagalhãesQueiCabr:2018:ClTeUs
TitleClassificação de texturas usando a métrica de centralidade closeness
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2021, Jan. 19
Number of Files2
Size1423 KiB
Context area
Author1 Magalhães, Whendell Feijó
2 Queiroz, Fabiane da Silva
3 Cabral, Raquel da Silva
Affiliation1 Universidade Federal de Alagoas - Campus Arapiraca
2 Universidade Federal de Alagoas - Centro de Ciências Agrárias
3 Universidade Federal de Alagoas - Campus Arapiraca
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresswhendell.magalhaes@arapiraca.ufal.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2018-10-22 22:49:40 :: whendell.magalhaes@arapiraca.ufal.br -> administrator :: 2018
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsredes complexas, classificação de texturas, centralidade closeness.
AbstractIn 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.
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Classificação de Texturas Usando a Métrica de Centralidade Closeness.pdf 22/10/2018 19:38 711.4 KiB 
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3S49SBP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3S49SBP
Languagept
Target FileClassificação de Texturas Usando a Métrica de Centralidade Closeness.pdf
User Groupwhendell.magalhaes@arapiraca.ufal.br
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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