Explorando abordagens de classificação contextual para floresta de caminhos ótimos
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/8098 |
Resumo: | Pattern recognition techniques have been widely studied and disseminated in order to develop ways to improve the e ectiveness of the pattern classi ers using labeled samples. However, such techniques usually work following the premise that the samples are independent and identically distributed in the feature space, taking into account only the local properties of the image and no information about the correlations between neighboring pixels are employed. The Optimum-Path Forest (OPF) classi er models the instances as the nodes of a graph, being the problem now is reduced to a partition of this graph. Although there are approaches that consider the context in the pattern recognition process, there is no such version for Optimum-Path Forest up to date. Thus, one of the main goal of the presented thesis is to propose a contextual version for the OPF classi er, which would employes contextual informations to support the data classi cation task using methods based on information theory and Markov Random Fields for such purpose. Since the Markov models are parameter-dependent and it is not a straightforward task to nd out the optimal values for such parameters because can assume in nite solutions, another contribution of this work is to propose an approach for modeling the process of nd out the parameters as a optimization problem, being the tness function to be maximized is the OPF accuracy over a labeled set. The results obtained by contextual classi cation were better than traditional classi cation results, as well as the optimization methods applied seemed to be a good alternative to ne-tune parameters of the Markov models as well. |
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Osaku, DanielPapa, João Paulohttp://lattes.cnpq.br/9039182932747194Levada, Alexandre Luís Magalhãeshttp://lattes.cnpq.br/3341441596395463http://lattes.cnpq.br/0086432334240096e8f432a5-f4d2-41b4-8ec7-cafed4cf35c92016-10-21T12:02:11Z2016-10-21T12:02:11Z2016-06-24OSAKU, Daniel. Explorando abordagens de classificação contextual para floresta de caminhos ótimos. 2016. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/ufscar/8098.https://repositorio.ufscar.br/handle/ufscar/8098Pattern recognition techniques have been widely studied and disseminated in order to develop ways to improve the e ectiveness of the pattern classi ers using labeled samples. However, such techniques usually work following the premise that the samples are independent and identically distributed in the feature space, taking into account only the local properties of the image and no information about the correlations between neighboring pixels are employed. The Optimum-Path Forest (OPF) classi er models the instances as the nodes of a graph, being the problem now is reduced to a partition of this graph. Although there are approaches that consider the context in the pattern recognition process, there is no such version for Optimum-Path Forest up to date. Thus, one of the main goal of the presented thesis is to propose a contextual version for the OPF classi er, which would employes contextual informations to support the data classi cation task using methods based on information theory and Markov Random Fields for such purpose. Since the Markov models are parameter-dependent and it is not a straightforward task to nd out the optimal values for such parameters because can assume in nite solutions, another contribution of this work is to propose an approach for modeling the process of nd out the parameters as a optimization problem, being the tness function to be maximized is the OPF accuracy over a labeled set. The results obtained by contextual classi cation were better than traditional classi cation results, as well as the optimization methods applied seemed to be a good alternative to ne-tune parameters of the Markov models as well.Técnicas de reconhecimento de padrões em imagens foram amplamente estudadas e difundidas com o intuito de desenvolver maneiras para melhorar a eficácia dos classificadores de padrões utilizando amostras rotuladas. Contudo, muitas dessas técnicas classificam seguindo a premissa de que as instâncias são independentes e identicamente distribuídas no espaço de características, levando-se em consideração apenas as propriedades locais da imagem e nenhuma informação sobre as correlações entre pixels vizinhos são utilizadas. O classificador Floresta de Caminhos Ótimos modela as instâncias como sendo os nós de um grafo, sendo que o problema agora é reduzido para um particionamento desse grafo. Embora existam abordagens que levam em consideração o contexto no processo de reconhecimento de padrões, ainda não existe nenhuma versão do classificador Floresta de Caminhos Ótimos nesse sentido. Assim sendo, um dos objetivos principais da presente tese de doutorado _e propor uma versão contextual para a técnica Floresta de Caminhos Ótimos, a qual faria uso então de informações contextuais para auxiliar na tarefa de classificação de dados utilizando métodos baseados em Teoria da Informação e Campos Aleatórios Markovianos para tal finalidade. Uma vez que os modelos Markovianos são dependentes de parâmetros e não é possível encontrar o valor ótimo, pois podem assumir infinitas soluções, uma outra principal contribuição deste trabalho é propor uma abordagem. para modelar o processo de encontrar tais parâmetros como sendo um problema de otimização, em que a função de aptidão a ser maximizada é a acurasse da técnica Floresta de Caminhos Ótimos sobre um conjunto rotulado. Os resultados obtidos foram melhores para classificação contextual do que para o método de classificação tradicional, bem como também os métodos de otimização aplicados demonstraram ser uma boa alternativa para a definição dos parâmetros dos modelos Markovianos.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)porUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Ciência da Computação - PPGCCUFSCarClassificação contextual de imagensSensoriamento remotoRessonância magnéticaCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAOExplorando abordagens de classificação contextual para floresta de caminhos ótimosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisOnline600600a26a6b97-f6e5-4bd7-9c5a-876ad8cf02fdinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARLICENSElicense.txtlicense.txttext/plain; charset=utf-81957https://repositorio.ufscar.br/bitstream/ufscar/8098/2/license.txtae0398b6f8b235e40ad82cba6c50031dMD52ORIGINALTeseDO.pdfTeseDO.pdfapplication/pdf5356561https://repositorio.ufscar.br/bitstream/ufscar/8098/1/TeseDO.pdfb2dfa29e88731d9e552bdb0ce8eda412MD51TEXTTeseDO.pdf.txtTeseDO.pdf.txtExtracted texttext/plain262684https://repositorio.ufscar.br/bitstream/ufscar/8098/3/TeseDO.pdf.txt836c0e4c35c2c9885d5c0a94904578f8MD53THUMBNAILTeseDO.pdf.jpgTeseDO.pdf.jpgIM Thumbnailimage/jpeg9654https://repositorio.ufscar.br/bitstream/ufscar/8098/4/TeseDO.pdf.jpgf34451573efae908679aacc003ded04eMD54ufscar/80982023-09-18 18:31:54.095oai:repositorio.ufscar.br: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Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:31:54Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.por.fl_str_mv |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
title |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
spellingShingle |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos Osaku, Daniel Classificação contextual de imagens Sensoriamento remoto Ressonância magnética CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
title_short |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
title_full |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
title_fullStr |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
title_full_unstemmed |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
title_sort |
Explorando abordagens de classificação contextual para floresta de caminhos ótimos |
author |
Osaku, Daniel |
author_facet |
Osaku, Daniel |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/0086432334240096 |
dc.contributor.author.fl_str_mv |
Osaku, Daniel |
dc.contributor.advisor1.fl_str_mv |
Papa, João Paulo |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9039182932747194 |
dc.contributor.advisor-co1.fl_str_mv |
Levada, Alexandre Luís Magalhães |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/3341441596395463 |
dc.contributor.authorID.fl_str_mv |
e8f432a5-f4d2-41b4-8ec7-cafed4cf35c9 |
contributor_str_mv |
Papa, João Paulo Levada, Alexandre Luís Magalhães |
dc.subject.por.fl_str_mv |
Classificação contextual de imagens Sensoriamento remoto Ressonância magnética |
topic |
Classificação contextual de imagens Sensoriamento remoto Ressonância magnética CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO |
description |
Pattern recognition techniques have been widely studied and disseminated in order to develop ways to improve the e ectiveness of the pattern classi ers using labeled samples. However, such techniques usually work following the premise that the samples are independent and identically distributed in the feature space, taking into account only the local properties of the image and no information about the correlations between neighboring pixels are employed. The Optimum-Path Forest (OPF) classi er models the instances as the nodes of a graph, being the problem now is reduced to a partition of this graph. Although there are approaches that consider the context in the pattern recognition process, there is no such version for Optimum-Path Forest up to date. Thus, one of the main goal of the presented thesis is to propose a contextual version for the OPF classi er, which would employes contextual informations to support the data classi cation task using methods based on information theory and Markov Random Fields for such purpose. Since the Markov models are parameter-dependent and it is not a straightforward task to nd out the optimal values for such parameters because can assume in nite solutions, another contribution of this work is to propose an approach for modeling the process of nd out the parameters as a optimization problem, being the tness function to be maximized is the OPF accuracy over a labeled set. The results obtained by contextual classi cation were better than traditional classi cation results, as well as the optimization methods applied seemed to be a good alternative to ne-tune parameters of the Markov models as well. |
publishDate |
2016 |
dc.date.accessioned.fl_str_mv |
2016-10-21T12:02:11Z |
dc.date.available.fl_str_mv |
2016-10-21T12:02:11Z |
dc.date.issued.fl_str_mv |
2016-06-24 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
OSAKU, Daniel. Explorando abordagens de classificação contextual para floresta de caminhos ótimos. 2016. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/ufscar/8098. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/8098 |
identifier_str_mv |
OSAKU, Daniel. Explorando abordagens de classificação contextual para floresta de caminhos ótimos. 2016. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/ufscar/8098. |
url |
https://repositorio.ufscar.br/handle/ufscar/8098 |
dc.language.iso.fl_str_mv |
por |
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por |
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600 600 |
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a26a6b97-f6e5-4bd7-9c5a-876ad8cf02fd |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos Câmpus São Carlos |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciência da Computação - PPGCC |
dc.publisher.initials.fl_str_mv |
UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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