A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest

Detalhes bibliográficos
Autor(a) principal: Osaku, Daniel
Data de Publicação: 2016
Outros Autores: Levada, Alexandre L.M., Papa, Joao Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ISCAS.2016.7527410
http://hdl.handle.net/11449/234505
Resumo: Contextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest classifier together with Markov Random Fields (MRF) that surpasses its naïve version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach an outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.
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spelling A block-based Markov random field model estimation for contextual classification using Optimum-Path ForestLandcover ClassificationOptimum-Path ForestPattern ClassificationContextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest classifier together with Markov Random Fields (MRF) that surpasses its naïve version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach an outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Computer Science Federal University of São CarlosDepartment of Computing São Paulo State UniversityDepartment of Computing São Paulo State UniversityFAPESP: 2012/06472-9FAPESP: 2014/16250-9FAPESP: 2015/50319-9Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (UNESP)Osaku, DanielLevada, Alexandre L.M.Papa, Joao Paulo [UNESP]2022-05-02T20:23:59Z2022-05-02T20:23:59Z2016-07-29info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject994-997http://dx.doi.org/10.1109/ISCAS.2016.7527410Proceedings - IEEE International Symposium on Circuits and Systems, v. 2016-July, p. 994-997.0271-4310http://hdl.handle.net/11449/23450510.1109/ISCAS.2016.75274102-s2.0-84983453322Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - IEEE International Symposium on Circuits and Systemsinfo:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/234505Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:48:48.907678Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
title A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
spellingShingle A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
Osaku, Daniel
Landcover Classification
Optimum-Path Forest
Pattern Classification
title_short A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
title_full A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
title_fullStr A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
title_full_unstemmed A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
title_sort A block-based Markov random field model estimation for contextual classification using Optimum-Path Forest
author Osaku, Daniel
author_facet Osaku, Daniel
Levada, Alexandre L.M.
Papa, Joao Paulo [UNESP]
author_role author
author2 Levada, Alexandre L.M.
Papa, Joao Paulo [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Carlos (UFSCar)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Osaku, Daniel
Levada, Alexandre L.M.
Papa, Joao Paulo [UNESP]
dc.subject.por.fl_str_mv Landcover Classification
Optimum-Path Forest
Pattern Classification
topic Landcover Classification
Optimum-Path Forest
Pattern Classification
description Contextual image classification aims at considering the information about nearby samples in the learning process in order to provide more accurate results. In this paper, we propose a locally-adaptive Optimum-Path Forest classifier together with Markov Random Fields (MRF) that surpasses its naïve version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach an outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.
publishDate 2016
dc.date.none.fl_str_mv 2016-07-29
2022-05-02T20:23:59Z
2022-05-02T20:23:59Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ISCAS.2016.7527410
Proceedings - IEEE International Symposium on Circuits and Systems, v. 2016-July, p. 994-997.
0271-4310
http://hdl.handle.net/11449/234505
10.1109/ISCAS.2016.7527410
2-s2.0-84983453322
url http://dx.doi.org/10.1109/ISCAS.2016.7527410
http://hdl.handle.net/11449/234505
identifier_str_mv Proceedings - IEEE International Symposium on Circuits and Systems, v. 2016-July, p. 994-997.
0271-4310
10.1109/ISCAS.2016.7527410
2-s2.0-84983453322
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings - IEEE International Symposium on Circuits and Systems
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 994-997
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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