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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/20462
Ver los metadatos del registro completo
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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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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eng |
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eng |
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info:eu-repo/semantics/altIdentifier/isbn/978-950-763-075-0 |
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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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