Appling parallelism in image mining

Autores
Fernández, Jacqueline; Miranda, Natalia Carolina; Guerrero, Roberto A.; Piccoli, María Fabiana
Año de publicación
2007
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Image mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from computer vision and image processing areas. Image mining deals with the extraction of image patterns from a large collection of images, whereas the focus of computer vision and image processing is in understanding and/or extracting specific features from a single image. On the other hand it might be thought that it is much related to content-based retrieval area, since both deals with large image collections. Nevertheless, image mining goes beyond the simple fact of recovering relevant images, the goal is the discovery of image patterns that are significant in a given collection of images. As a result, an image mining systems implies lots of tasks to be done in a regular time. Images provide a natural source of parallelism; so the use of parallelism in every or some mining tasks might be a good option to reduce the cost and overhead of the whole image mining process. At this work we will try to draw the image minnig problem: its computational cost, and to propose a possible global or local parallel solution.
Eje: Procesamiento Concurrente, Paralelo y Distribuido
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Image mining
image indexing and retrieval
object recognition
image clustering
association rule mining
parallel systems
parallel techniques
Parallel
Distributed
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/20462

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spelling Appling parallelism in image miningFernández, JacquelineMiranda, Natalia CarolinaGuerrero, Roberto A.Piccoli, María FabianaCiencias InformáticasImage miningimage indexing and retrievalobject recognitionimage clusteringassociation rule miningparallel systemsparallel techniquesParallelDistributedImage mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from computer vision and image processing areas. Image mining deals with the extraction of image patterns from a large collection of images, whereas the focus of computer vision and image processing is in understanding and/or extracting specific features from a single image. On the other hand it might be thought that it is much related to content-based retrieval area, since both deals with large image collections. Nevertheless, image mining goes beyond the simple fact of recovering relevant images, the goal is the discovery of image patterns that are significant in a given collection of images. As a result, an image mining systems implies lots of tasks to be done in a regular time. Images provide a natural source of parallelism; so the use of parallelism in every or some mining tasks might be a good option to reduce the cost and overhead of the whole image mining process. At this work we will try to draw the image minnig problem: its computational cost, and to propose a possible global or local parallel solution.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI)2007-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf578-582http://sedici.unlp.edu.ar/handle/10915/20462enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-763-075-0info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:15Zoai:sedici.unlp.edu.ar:10915/20462Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:15.491SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Appling parallelism in image mining
title Appling parallelism in image mining
spellingShingle Appling parallelism in image mining
Fernández, Jacqueline
Ciencias Informáticas
Image mining
image indexing and retrieval
object recognition
image clustering
association rule mining
parallel systems
parallel techniques
Parallel
Distributed
title_short Appling parallelism in image mining
title_full Appling parallelism in image mining
title_fullStr Appling parallelism in image mining
title_full_unstemmed Appling parallelism in image mining
title_sort Appling parallelism in image mining
dc.creator.none.fl_str_mv Fernández, Jacqueline
Miranda, Natalia Carolina
Guerrero, Roberto A.
Piccoli, María Fabiana
author Fernández, Jacqueline
author_facet Fernández, Jacqueline
Miranda, Natalia Carolina
Guerrero, Roberto A.
Piccoli, María Fabiana
author_role author
author2 Miranda, Natalia Carolina
Guerrero, Roberto A.
Piccoli, María Fabiana
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Image mining
image indexing and retrieval
object recognition
image clustering
association rule mining
parallel systems
parallel techniques
Parallel
Distributed
topic Ciencias Informáticas
Image mining
image indexing and retrieval
object recognition
image clustering
association rule mining
parallel systems
parallel techniques
Parallel
Distributed
dc.description.none.fl_txt_mv Image mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from computer vision and image processing areas. Image mining deals with the extraction of image patterns from a large collection of images, whereas the focus of computer vision and image processing is in understanding and/or extracting specific features from a single image. On the other hand it might be thought that it is much related to content-based retrieval area, since both deals with large image collections. Nevertheless, image mining goes beyond the simple fact of recovering relevant images, the goal is the discovery of image patterns that are significant in a given collection of images. As a result, an image mining systems implies lots of tasks to be done in a regular time. Images provide a natural source of parallelism; so the use of parallelism in every or some mining tasks might be a good option to reduce the cost and overhead of the whole image mining process. At this work we will try to draw the image minnig problem: its computational cost, and to propose a possible global or local parallel solution.
Eje: Procesamiento Concurrente, Paralelo y Distribuido
Red de Universidades con Carreras en Informática (RedUNCI)
description Image mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from computer vision and image processing areas. Image mining deals with the extraction of image patterns from a large collection of images, whereas the focus of computer vision and image processing is in understanding and/or extracting specific features from a single image. On the other hand it might be thought that it is much related to content-based retrieval area, since both deals with large image collections. Nevertheless, image mining goes beyond the simple fact of recovering relevant images, the goal is the discovery of image patterns that are significant in a given collection of images. As a result, an image mining systems implies lots of tasks to be done in a regular time. Images provide a natural source of parallelism; so the use of parallelism in every or some mining tasks might be a good option to reduce the cost and overhead of the whole image mining process. At this work we will try to draw the image minnig problem: its computational cost, and to propose a possible global or local parallel solution.
publishDate 2007
dc.date.none.fl_str_mv 2007-05
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-763-075-0
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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578-582
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