Segmentation of color images by chromaticity features using self-organizing maps

Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors....

Full description

Autores:
García-Lamont, Farid
Cuevas Rasgado, Alma Delia
Niño Membrillo, Yedid Erandini
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/67618
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/67618
http://bdigital.unal.edu.co/68647/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
Segmentation of color images
color spaces
competitive neural networks
Segmentación de imágenes de color
espacios de color
redes neuronales competitivas
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_a16ae9285807b3a020b0d0e23a18a1b9
oai_identifier_str oai:repositorio.unal.edu.co:unal/67618
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Segmentation of color images by chromaticity features using self-organizing maps
title Segmentation of color images by chromaticity features using self-organizing maps
spellingShingle Segmentation of color images by chromaticity features using self-organizing maps
62 Ingeniería y operaciones afines / Engineering
Segmentation of color images
color spaces
competitive neural networks
Segmentación de imágenes de color
espacios de color
redes neuronales competitivas
title_short Segmentation of color images by chromaticity features using self-organizing maps
title_full Segmentation of color images by chromaticity features using self-organizing maps
title_fullStr Segmentation of color images by chromaticity features using self-organizing maps
title_full_unstemmed Segmentation of color images by chromaticity features using self-organizing maps
title_sort Segmentation of color images by chromaticity features using self-organizing maps
dc.creator.fl_str_mv García-Lamont, Farid
Cuevas Rasgado, Alma Delia
Niño Membrillo, Yedid Erandini
dc.contributor.author.spa.fl_str_mv García-Lamont, Farid
Cuevas Rasgado, Alma Delia
Niño Membrillo, Yedid Erandini
dc.subject.ddc.spa.fl_str_mv 62 Ingeniería y operaciones afines / Engineering
topic 62 Ingeniería y operaciones afines / Engineering
Segmentation of color images
color spaces
competitive neural networks
Segmentación de imágenes de color
espacios de color
redes neuronales competitivas
dc.subject.proposal.spa.fl_str_mv Segmentation of color images
color spaces
competitive neural networks
Segmentación de imágenes de color
espacios de color
redes neuronales competitivas
description Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.
publishDate 2016
dc.date.issued.spa.fl_str_mv 2016-05-01
dc.date.accessioned.spa.fl_str_mv 2019-07-03T04:41:26Z
dc.date.available.spa.fl_str_mv 2019-07-03T04:41:26Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.spa.fl_str_mv ISSN: 2248-8723
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/67618
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/68647/
identifier_str_mv ISSN: 2248-8723
url https://repositorio.unal.edu.co/handle/unal/67618
http://bdigital.unal.edu.co/68647/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/ingeinv/article/view/55746
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación
Ingeniería e Investigación
dc.relation.references.spa.fl_str_mv García-Lamont, Farid and Cuevas Rasgado, Alma Delia and Niño Membrillo, Yedid Erandini (2016) Segmentation of color images by chromaticity features using self-organizing maps. Ingeniería e Investigación, 36 (2). pp. 78-89. ISSN 2248-8723
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ingeniería
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/67618/1/55746-303227-1-PB.pdf
https://repositorio.unal.edu.co/bitstream/unal/67618/2/55746-303227-1-PB.pdf.jpg
bitstream.checksum.fl_str_mv 83fbda3b661b46011fd295a6ce82ef8a
7c1938ab3d5c458a17b25d3691180514
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814090039924097024
spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2García-Lamont, Farid506040c4-51ac-4a3d-84a1-6e27f1e5bac1300Cuevas Rasgado, Alma Delia04307d02-5807-4f73-b9d4-2853b86533c2300Niño Membrillo, Yedid Erandini6a351925-ab28-404f-a7fd-e7f5745eadb63002019-07-03T04:41:26Z2019-07-03T04:41:26Z2016-05-01ISSN: 2248-8723https://repositorio.unal.edu.co/handle/unal/67618http://bdigital.unal.edu.co/68647/Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.Usualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ingenieríahttps://revistas.unal.edu.co/index.php/ingeinv/article/view/55746Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e InvestigaciónIngeniería e InvestigaciónGarcía-Lamont, Farid and Cuevas Rasgado, Alma Delia and Niño Membrillo, Yedid Erandini (2016) Segmentation of color images by chromaticity features using self-organizing maps. Ingeniería e Investigación, 36 (2). pp. 78-89. ISSN 2248-872362 Ingeniería y operaciones afines / EngineeringSegmentation of color imagescolor spacescompetitive neural networksSegmentación de imágenes de colorespacios de colorredes neuronales competitivasSegmentation of color images by chromaticity features using self-organizing mapsArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL55746-303227-1-PB.pdfapplication/pdf1337477https://repositorio.unal.edu.co/bitstream/unal/67618/1/55746-303227-1-PB.pdf83fbda3b661b46011fd295a6ce82ef8aMD51THUMBNAIL55746-303227-1-PB.pdf.jpg55746-303227-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg8488https://repositorio.unal.edu.co/bitstream/unal/67618/2/55746-303227-1-PB.pdf.jpg7c1938ab3d5c458a17b25d3691180514MD52unal/67618oai:repositorio.unal.edu.co:unal/676182023-05-30 23:03:22.212Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co