Classification of color textures with random field models and neural networks

Autores
Hernandez, Orlando J.; Cook, John; Griffin, Michael; De Rama, Cynthia; McGovern, Michael
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.
Facultad de Informática
Materia
Ciencias Informáticas
Color, shading, shadowing, and texture
Neural nets
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9587

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spelling Classification of color textures with random field models and neural networksHernandez, Orlando J.Cook, JohnGriffin, MichaelDe Rama, CynthiaMcGovern, MichaelCiencias InformáticasColor, shading, shadowing, and textureNeural netsA number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.Facultad de Informática2005-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf150-157http://sedici.unlp.edu.ar/handle/10915/9587enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct05-6.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:43:21Zoai:sedici.unlp.edu.ar:10915/9587Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:43:21.609SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Classification of color textures with random field models and neural networks
title Classification of color textures with random field models and neural networks
spellingShingle Classification of color textures with random field models and neural networks
Hernandez, Orlando J.
Ciencias Informáticas
Color, shading, shadowing, and texture
Neural nets
title_short Classification of color textures with random field models and neural networks
title_full Classification of color textures with random field models and neural networks
title_fullStr Classification of color textures with random field models and neural networks
title_full_unstemmed Classification of color textures with random field models and neural networks
title_sort Classification of color textures with random field models and neural networks
dc.creator.none.fl_str_mv Hernandez, Orlando J.
Cook, John
Griffin, Michael
De Rama, Cynthia
McGovern, Michael
author Hernandez, Orlando J.
author_facet Hernandez, Orlando J.
Cook, John
Griffin, Michael
De Rama, Cynthia
McGovern, Michael
author_role author
author2 Cook, John
Griffin, Michael
De Rama, Cynthia
McGovern, Michael
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Color, shading, shadowing, and texture
Neural nets
topic Ciencias Informáticas
Color, shading, shadowing, and texture
Neural nets
dc.description.none.fl_txt_mv A number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.
Facultad de Informática
description A number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.
publishDate 2005
dc.date.none.fl_str_mv 2005-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
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status_str publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
150-157
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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