On the influence of Markovian models for contextual-based Optimum-Path Forest classification

Detalhes bibliográficos
Autor(a) principal: Osaku, D.
Data de Publicação: 2014
Outros Autores: Levada, A. L.M., Papa, J. P. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/168182
Resumo: Contextual classification considers the information about a sample’s neighborhood to improve standard pixel-based classification approaches. In this work, we evaluated four different Markovian models for Optimum-Path Forest contextual classification considering land use recognition in remote sensing data. Some insights about the situations in which each of them should be applied are stated, as well as the idea behind them is explained.
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spelling On the influence of Markovian models for contextual-based Optimum-Path Forest classificationContextual classificationMarkov Random FieldsOptimum-Path ForestContextual classification considers the information about a sample’s neighborhood to improve standard pixel-based classification approaches. In this work, we evaluated four different Markovian models for Optimum-Path Forest contextual classification considering land use recognition in remote sensing data. Some insights about the situations in which each of them should be applied are stated, as well as the idea behind them is explained.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federal University of São Carlos – UFSCar Department of Computer ScienceSão Paulo State University – UNESP Department of ComputingSão Paulo State University – UNESP Department of ComputingCNPq: 303182/2011-3CNPq: 470571/2013-6Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Osaku, D.Levada, A. L.M.Papa, J. P. [UNESP]2018-12-11T16:40:07Z2018-12-11T16:40:07Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject462-469Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 462-469.1611-33490302-9743http://hdl.handle.net/11449/1681822-s2.0-84949143701Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:19Zoai:repositorio.unesp.br:11449/168182Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:19Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv On the influence of Markovian models for contextual-based Optimum-Path Forest classification
title On the influence of Markovian models for contextual-based Optimum-Path Forest classification
spellingShingle On the influence of Markovian models for contextual-based Optimum-Path Forest classification
Osaku, D.
Contextual classification
Markov Random Fields
Optimum-Path Forest
title_short On the influence of Markovian models for contextual-based Optimum-Path Forest classification
title_full On the influence of Markovian models for contextual-based Optimum-Path Forest classification
title_fullStr On the influence of Markovian models for contextual-based Optimum-Path Forest classification
title_full_unstemmed On the influence of Markovian models for contextual-based Optimum-Path Forest classification
title_sort On the influence of Markovian models for contextual-based Optimum-Path Forest classification
author Osaku, D.
author_facet Osaku, D.
Levada, A. L.M.
Papa, J. P. [UNESP]
author_role author
author2 Levada, A. L.M.
Papa, J. P. [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, D.
Levada, A. L.M.
Papa, J. P. [UNESP]
dc.subject.por.fl_str_mv Contextual classification
Markov Random Fields
Optimum-Path Forest
topic Contextual classification
Markov Random Fields
Optimum-Path Forest
description Contextual classification considers the information about a sample’s neighborhood to improve standard pixel-based classification approaches. In this work, we evaluated four different Markovian models for Optimum-Path Forest contextual classification considering land use recognition in remote sensing data. Some insights about the situations in which each of them should be applied are stated, as well as the idea behind them is explained.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2018-12-11T16:40:07Z
2018-12-11T16:40:07Z
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 462-469.
1611-3349
0302-9743
http://hdl.handle.net/11449/168182
2-s2.0-84949143701
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 462-469.
1611-3349
0302-9743
2-s2.0-84949143701
url http://hdl.handle.net/11449/168182
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 462-469
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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