Shape based image retrieval and classification
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://repositorio-aberto.up.pt/handle/10216/23326 |
Resumo: | Content based retrieval and recognition of objects represented in images is a challenging problem making it an active research topic. Shape analysis is one of the main approaches to the problem. In this paper we propose the use of a reduced set of features to describe 2D shapes in images. The design of the proposed technique aims to result in a short and simple to extract shape description. We conducted several experiments for both retrieval and recognition tasks and the results obtained demonstrate usefulness and competiveness against existing descriptors. For the retrieval experiment the achieved bulls eye performance is about 60%. Recognition was tested with three different classifiers: decision trees (DT), k-nearest neighbor (kNN) and support vector machines (SVM). Estimated mean accuracies range from 69% to 86% (using 10-fold cross validation). The SVM classifier presents the best performance, followed by the simple kNN classifier. |
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7160 |
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Shape based image retrieval and classificationProcessamento de imagem, Outras ciências da engenharia e tecnologiasImage processing, Other engineering and technologiesContent based retrieval and recognition of objects represented in images is a challenging problem making it an active research topic. Shape analysis is one of the main approaches to the problem. In this paper we propose the use of a reduced set of features to describe 2D shapes in images. The design of the proposed technique aims to result in a short and simple to extract shape description. We conducted several experiments for both retrieval and recognition tasks and the results obtained demonstrate usefulness and competiveness against existing descriptors. For the retrieval experiment the achieved bulls eye performance is about 60%. Recognition was tested with three different classifiers: decision trees (DT), k-nearest neighbor (kNN) and support vector machines (SVM). Estimated mean accuracies range from 69% to 86% (using 10-fold cross validation). The SVM classifier presents the best performance, followed by the simple kNN classifier.20102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/23326engJoão Ferreira de Carvalho Castro NunesPedro Miguel MoreiraJoão Manuel Ribeiro da Silva Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:42:14Zoai:repositorio-aberto.up.pt:10216/23326Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:30:02.918473Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Shape based image retrieval and classification |
title |
Shape based image retrieval and classification |
spellingShingle |
Shape based image retrieval and classification João Ferreira de Carvalho Castro Nunes Processamento de imagem, Outras ciências da engenharia e tecnologias Image processing, Other engineering and technologies |
title_short |
Shape based image retrieval and classification |
title_full |
Shape based image retrieval and classification |
title_fullStr |
Shape based image retrieval and classification |
title_full_unstemmed |
Shape based image retrieval and classification |
title_sort |
Shape based image retrieval and classification |
author |
João Ferreira de Carvalho Castro Nunes |
author_facet |
João Ferreira de Carvalho Castro Nunes Pedro Miguel Moreira João Manuel Ribeiro da Silva Tavares |
author_role |
author |
author2 |
Pedro Miguel Moreira João Manuel Ribeiro da Silva Tavares |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
João Ferreira de Carvalho Castro Nunes Pedro Miguel Moreira João Manuel Ribeiro da Silva Tavares |
dc.subject.por.fl_str_mv |
Processamento de imagem, Outras ciências da engenharia e tecnologias Image processing, Other engineering and technologies |
topic |
Processamento de imagem, Outras ciências da engenharia e tecnologias Image processing, Other engineering and technologies |
description |
Content based retrieval and recognition of objects represented in images is a challenging problem making it an active research topic. Shape analysis is one of the main approaches to the problem. In this paper we propose the use of a reduced set of features to describe 2D shapes in images. The design of the proposed technique aims to result in a short and simple to extract shape description. We conducted several experiments for both retrieval and recognition tasks and the results obtained demonstrate usefulness and competiveness against existing descriptors. For the retrieval experiment the achieved bulls eye performance is about 60%. Recognition was tested with three different classifiers: decision trees (DT), k-nearest neighbor (kNN) and support vector machines (SVM). Estimated mean accuracies range from 69% to 86% (using 10-fold cross validation). The SVM classifier presents the best performance, followed by the simple kNN classifier. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010 2010-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio-aberto.up.pt/handle/10216/23326 |
url |
https://repositorio-aberto.up.pt/handle/10216/23326 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799136210726158337 |