A Block-based Markov Random Field Model Estimation for Contextual Classification Using Optimum-Path Forest
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/165406 |
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 naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.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 naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version. |
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Repositório Institucional da UNESP |
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A Block-based Markov Random Field Model Estimation for Contextual Classification Using Optimum-Path ForestPattern ClassificationOptimum-Path ForestLand-cover ClassificatonContextual 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 naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.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 naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.Univ Fed Sao Carlos, Dept Comp Sci, BR-13560 Sao Carlos, SP, BrazilSao Paulo State Univ, Dept Comp, Sao Paulo, SP, BrazilSao Paulo State Univ, Dept Comp, Sao Paulo, SP, BrazilIeeeUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Osaku, DanielLevada, Alexandre L. M.Papa, Joao Paulo [UNESP]IEEE2018-11-28T00:57:54Z2018-11-28T00:57:54Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject994-9972016 Ieee International Symposium On Circuits And Systems (iscas). New York: Ieee, p. 994-997, 2016.0271-4302http://hdl.handle.net/11449/165406WOS:000390094701032Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2016 Ieee International Symposium On Circuits And Systems (iscas)info:eu-repo/semantics/openAccess2024-04-23T16:11:12Zoai:repositorio.unesp.br:11449/165406Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:21:25.053944Repositório 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 Pattern Classification Optimum-Path Forest Land-cover Classificaton |
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] IEEE |
author_role |
author |
author2 |
Levada, Alexandre L. M. Papa, Joao Paulo [UNESP] IEEE |
author2_role |
author 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] IEEE |
dc.subject.por.fl_str_mv |
Pattern Classification Optimum-Path Forest Land-cover Classificaton |
topic |
Pattern Classification Optimum-Path Forest Land-cover Classificaton |
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 naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can outperform previous results, as well as it can perform MRF parameter learning much faster than its former version.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 naive version, which was recently presented in the literature. The experimental results over four satellite images demonstrated the proposed approach can 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 |
2016-01-01 2018-11-28T00:57:54Z 2018-11-28T00:57:54Z |
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 |
2016 Ieee International Symposium On Circuits And Systems (iscas). New York: Ieee, p. 994-997, 2016. 0271-4302 http://hdl.handle.net/11449/165406 WOS:000390094701032 |
identifier_str_mv |
2016 Ieee International Symposium On Circuits And Systems (iscas). New York: Ieee, p. 994-997, 2016. 0271-4302 WOS:000390094701032 |
url |
http://hdl.handle.net/11449/165406 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2016 Ieee International Symposium On Circuits And Systems (iscas) |
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.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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 |
|
_version_ |
1808128351738527744 |