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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9587
Ver los metadatos del registro completo
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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 |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/9587 |
url |
http://sedici.unlp.edu.ar/handle/10915/9587 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct05-6.pdf 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 |
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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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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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