When data do not bring information: A case study in markov random fields estimation

Autores
Gimenez Romero, Javier Alejandro; Frery, Alejandro César; Flesia, Ana Georgina
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Potts model is frequently used to describe the behavior of image classes, since it allows to incorporate contextual information linking neighboring pixels in a simple way. Its isotropic version has only one real parameter β, known as smoothness parameter or inverse temperature, which regulates the classes map homogeneity. The classes are unavailable and estimating them is central in important image processing procedures as, for instance, image classification. Methods for estimating the classes which stem from a Bayesian approach under the Potts model require to adequately specify a value for β. The estimation of such parameter can be efficiently made solving the pseudo maximum-likelihood (PML) equations in two different schemes, using the prior or the posterior model. Having only radiometric data available, the first scheme needs the computation of an initial segmentation, whereas the second uses both the segmentation and the radiometric data to make the estimation. In this paper, we compare these two PML estimators by computing the mean-square error (MSE), bias, and sensitivity to deviations from the hypothesis of the model. We conclude that the use of extra data does not improve the accuracy of the PML; moreover, under gross deviations from the model, this extra information introduces unpredictable distortions and bias.
Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Potts Model
Pseudo-Likelihood
Segmentation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/38298
Acceder
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network_name_str CONICET Digital (CONICET)
spelling When data do not bring information: A case study in markov random fields estimationGimenez Romero, Javier AlejandroFrery, Alejandro CésarFlesia, Ana GeorginaPotts ModelPseudo-LikelihoodSegmentationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The Potts model is frequently used to describe the behavior of image classes, since it allows to incorporate contextual information linking neighboring pixels in a simple way. Its isotropic version has only one real parameter β, known as smoothness parameter or inverse temperature, which regulates the classes map homogeneity. The classes are unavailable and estimating them is central in important image processing procedures as, for instance, image classification. Methods for estimating the classes which stem from a Bayesian approach under the Potts model require to adequately specify a value for β. The estimation of such parameter can be efficiently made solving the pseudo maximum-likelihood (PML) equations in two different schemes, using the prior or the posterior model. Having only radiometric data available, the first scheme needs the computation of an initial segmentation, whereas the second uses both the segmentation and the radiometric data to make the estimation. In this paper, we compare these two PML estimators by computing the mean-square error (MSE), bias, and sensitivity to deviations from the hypothesis of the model. We conclude that the use of extra data does not improve the accuracy of the PML; moreover, under gross deviations from the model, this extra information introduces unpredictable distortions and bias.Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaInstitute of Electrical and Electronics Engineers2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/38298Gimenez Romero, Javier Alejandro; Frery, Alejandro César; Flesia, Ana Georgina; When data do not bring information: A case study in markov random fields estimation; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 8; 1; 1-2015; 195-2031939-1404CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2014.2323713info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6869017/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:40:06Zoai:ri.conicet.gov.ar:11336/38298instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:40:06.476CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv When data do not bring information: A case study in markov random fields estimation
title When data do not bring information: A case study in markov random fields estimation
spellingShingle When data do not bring information: A case study in markov random fields estimation
Gimenez Romero, Javier Alejandro
Potts Model
Pseudo-Likelihood
Segmentation
title_short When data do not bring information: A case study in markov random fields estimation
title_full When data do not bring information: A case study in markov random fields estimation
title_fullStr When data do not bring information: A case study in markov random fields estimation
title_full_unstemmed When data do not bring information: A case study in markov random fields estimation
title_sort When data do not bring information: A case study in markov random fields estimation
dc.creator.none.fl_str_mv Gimenez Romero, Javier Alejandro
Frery, Alejandro César
Flesia, Ana Georgina
author Gimenez Romero, Javier Alejandro
author_facet Gimenez Romero, Javier Alejandro
Frery, Alejandro César
Flesia, Ana Georgina
author_role author
author2 Frery, Alejandro César
Flesia, Ana Georgina
author2_role author
author
dc.subject.none.fl_str_mv Potts Model
Pseudo-Likelihood
Segmentation
topic Potts Model
Pseudo-Likelihood
Segmentation
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The Potts model is frequently used to describe the behavior of image classes, since it allows to incorporate contextual information linking neighboring pixels in a simple way. Its isotropic version has only one real parameter β, known as smoothness parameter or inverse temperature, which regulates the classes map homogeneity. The classes are unavailable and estimating them is central in important image processing procedures as, for instance, image classification. Methods for estimating the classes which stem from a Bayesian approach under the Potts model require to adequately specify a value for β. The estimation of such parameter can be efficiently made solving the pseudo maximum-likelihood (PML) equations in two different schemes, using the prior or the posterior model. Having only radiometric data available, the first scheme needs the computation of an initial segmentation, whereas the second uses both the segmentation and the radiometric data to make the estimation. In this paper, we compare these two PML estimators by computing the mean-square error (MSE), bias, and sensitivity to deviations from the hypothesis of the model. We conclude that the use of extra data does not improve the accuracy of the PML; moreover, under gross deviations from the model, this extra information introduces unpredictable distortions and bias.
Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The Potts model is frequently used to describe the behavior of image classes, since it allows to incorporate contextual information linking neighboring pixels in a simple way. Its isotropic version has only one real parameter β, known as smoothness parameter or inverse temperature, which regulates the classes map homogeneity. The classes are unavailable and estimating them is central in important image processing procedures as, for instance, image classification. Methods for estimating the classes which stem from a Bayesian approach under the Potts model require to adequately specify a value for β. The estimation of such parameter can be efficiently made solving the pseudo maximum-likelihood (PML) equations in two different schemes, using the prior or the posterior model. Having only radiometric data available, the first scheme needs the computation of an initial segmentation, whereas the second uses both the segmentation and the radiometric data to make the estimation. In this paper, we compare these two PML estimators by computing the mean-square error (MSE), bias, and sensitivity to deviations from the hypothesis of the model. We conclude that the use of extra data does not improve the accuracy of the PML; moreover, under gross deviations from the model, this extra information introduces unpredictable distortions and bias.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/38298
Gimenez Romero, Javier Alejandro; Frery, Alejandro César; Flesia, Ana Georgina; When data do not bring information: A case study in markov random fields estimation; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 8; 1; 1-2015; 195-203
1939-1404
CONICET Digital
CONICET
url http://hdl.handle.net/11336/38298
identifier_str_mv Gimenez Romero, Javier Alejandro; Frery, Alejandro César; Flesia, Ana Georgina; When data do not bring information: A case study in markov random fields estimation; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 8; 1; 1-2015; 195-203
1939-1404
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2014.2323713
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6869017/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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