Content based image retrieval through object features

Autores
Meenakshi , R.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
reseña artículo
Estado
versión publicada
Descripción
Digital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can roughly be classified into three categories such as text-based, content-based and semantic based. ARC-BC or convexity measures. The aim of this thesis to show that the rate of retrieval can be improved by combining various features than using a single characteristic. The proposed method combines colour, texture and geometric features to form a multidimension feature vector. (Párrafo extraído del texto a modo de resumen)
Facultad de Informática
Materia
Ciencias Informáticas
imagen digital
Almacenamiento y Recuperación de la Información
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:10915/41814
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/41814
network_acronym_str SEDICI
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network_name_str SEDICI (UNLP)
spelling Content based image retrieval through object featuresMeenakshi , R.Ciencias Informáticasimagen digitalAlmacenamiento y Recuperación de la InformaciónDigital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can roughly be classified into three categories such as text-based, content-based and semantic based. ARC-BC or convexity measures. The aim of this thesis to show that the rate of retrieval can be improved by combining various features than using a single characteristic. The proposed method combines colour, texture and geometric features to form a multidimension feature vector. <i>(Párrafo extraído del texto a modo de resumen)</i>Facultad de Informática2014-10info:eu-repo/semantics/reviewinfo:eu-repo/semantics/publishedVersionRevisionhttp://purl.org/coar/resource_type/c_dcae04bcinfo:ar-repo/semantics/resenaArticuloapplication/pdf109-110http://sedici.unlp.edu.ar/handle/10915/41814enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct14-TO1.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:UNLP2025-10-22T16:42:42Zoai:sedici.unlp.edu.ar:10915/41814Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:42:42.365SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Content based image retrieval through object features
title Content based image retrieval through object features
spellingShingle Content based image retrieval through object features
Meenakshi , R.
Ciencias Informáticas
imagen digital
Almacenamiento y Recuperación de la Información
title_short Content based image retrieval through object features
title_full Content based image retrieval through object features
title_fullStr Content based image retrieval through object features
title_full_unstemmed Content based image retrieval through object features
title_sort Content based image retrieval through object features
dc.creator.none.fl_str_mv Meenakshi , R.
author Meenakshi , R.
author_facet Meenakshi , R.
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
imagen digital
Almacenamiento y Recuperación de la Información
topic Ciencias Informáticas
imagen digital
Almacenamiento y Recuperación de la Información
dc.description.none.fl_txt_mv Digital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can roughly be classified into three categories such as text-based, content-based and semantic based. ARC-BC or convexity measures. The aim of this thesis to show that the rate of retrieval can be improved by combining various features than using a single characteristic. The proposed method combines colour, texture and geometric features to form a multidimension feature vector. <i>(Párrafo extraído del texto a modo de resumen)</i>
Facultad de Informática
description Digital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can roughly be classified into three categories such as text-based, content-based and semantic based. ARC-BC or convexity measures. The aim of this thesis to show that the rate of retrieval can be improved by combining various features than using a single characteristic. The proposed method combines colour, texture and geometric features to form a multidimension feature vector. <i>(Párrafo extraído del texto a modo de resumen)</i>
publishDate 2014
dc.date.none.fl_str_mv 2014-10
dc.type.none.fl_str_mv info:eu-repo/semantics/review
info:eu-repo/semantics/publishedVersion
Revision
http://purl.org/coar/resource_type/c_dcae04bc
info:ar-repo/semantics/resenaArticulo
format review
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/41814
url http://sedici.unlp.edu.ar/handle/10915/41814
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-Oct14-TO1.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
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
109-110
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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