A multi-level rank correlation measure for image retrieval
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/206095 |
Resumo: | Accurately ranking the most relevant elements in a given scenario often represents a central challenge in many applications, composing the core of retrieval systems. Once ranking structures encode relevant similarity information, measuring how correlated are two rank results represents a fundamental task, with diversified applications. In this work, we propose a new rank correlation measure called Multi-Level Rank Correlation Measure (MLCM), which employs a novel approach based on a multi-level analysis for estimating the correlation between ranked lists. While traditional weighted measures assign more relevance to top positions, our proposed approach goes beyond by considering the position at different levels in the ranked lists. The effectiveness of the proposed measure was assessed in unsupervised and weakly supervised learning tasks for image retrieval. The experimental evaluation considered 6 correlation measures as baselines, 3 different image datasets, and multiple features. The results are competitive or, in most of the cases, superior to the baselines, achieving significant effectiveness gains. |
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A multi-level rank correlation measure for image retrievalContent-based image retrievalInformation retrievalRank correlationUnsupervised learningAccurately ranking the most relevant elements in a given scenario often represents a central challenge in many applications, composing the core of retrieval systems. Once ranking structures encode relevant similarity information, measuring how correlated are two rank results represents a fundamental task, with diversified applications. In this work, we propose a new rank correlation measure called Multi-Level Rank Correlation Measure (MLCM), which employs a novel approach based on a multi-level analysis for estimating the correlation between ranked lists. While traditional weighted measures assign more relevance to top positions, our proposed approach goes beyond by considering the position at different levels in the ranked lists. The effectiveness of the proposed measure was assessed in unsupervised and weakly supervised learning tasks for image retrieval. The experimental evaluation considered 6 correlation measures as baselines, 3 different image datasets, and multiple features. The results are competitive or, in most of the cases, superior to the baselines, achieving significant effectiveness gains.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Statistics Applied Math. and Computing São Paulo State University (UNESP)Department of Statistics Applied Math. and Computing São Paulo State University (UNESP)FAPESP: #2017/25908-6FAPESP: #2018/15597-6FAPESP: #2019/11104-8FAPESP: #2020/11366-0CNPq: #308194/2017-9Universidade Estadual Paulista (Unesp)De Sá, Nikolas Gomes [UNESP]Valem, Lucas Pascotti [UNESP]Guimarães Pedronette, Daniel Carlos [UNESP]2021-06-25T10:26:25Z2021-06-25T10:26:25Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject370-378VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, v. 5, p. 370-378.http://hdl.handle.net/11449/2060952-s2.0-85102977558Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengVISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applicationsinfo:eu-repo/semantics/openAccess2021-10-22T20:56:23Zoai:repositorio.unesp.br:11449/206095Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:04:16.016072Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A multi-level rank correlation measure for image retrieval |
title |
A multi-level rank correlation measure for image retrieval |
spellingShingle |
A multi-level rank correlation measure for image retrieval De Sá, Nikolas Gomes [UNESP] Content-based image retrieval Information retrieval Rank correlation Unsupervised learning |
title_short |
A multi-level rank correlation measure for image retrieval |
title_full |
A multi-level rank correlation measure for image retrieval |
title_fullStr |
A multi-level rank correlation measure for image retrieval |
title_full_unstemmed |
A multi-level rank correlation measure for image retrieval |
title_sort |
A multi-level rank correlation measure for image retrieval |
author |
De Sá, Nikolas Gomes [UNESP] |
author_facet |
De Sá, Nikolas Gomes [UNESP] Valem, Lucas Pascotti [UNESP] Guimarães Pedronette, Daniel Carlos [UNESP] |
author_role |
author |
author2 |
Valem, Lucas Pascotti [UNESP] Guimarães Pedronette, Daniel Carlos [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
De Sá, Nikolas Gomes [UNESP] Valem, Lucas Pascotti [UNESP] Guimarães Pedronette, Daniel Carlos [UNESP] |
dc.subject.por.fl_str_mv |
Content-based image retrieval Information retrieval Rank correlation Unsupervised learning |
topic |
Content-based image retrieval Information retrieval Rank correlation Unsupervised learning |
description |
Accurately ranking the most relevant elements in a given scenario often represents a central challenge in many applications, composing the core of retrieval systems. Once ranking structures encode relevant similarity information, measuring how correlated are two rank results represents a fundamental task, with diversified applications. In this work, we propose a new rank correlation measure called Multi-Level Rank Correlation Measure (MLCM), which employs a novel approach based on a multi-level analysis for estimating the correlation between ranked lists. While traditional weighted measures assign more relevance to top positions, our proposed approach goes beyond by considering the position at different levels in the ranked lists. The effectiveness of the proposed measure was assessed in unsupervised and weakly supervised learning tasks for image retrieval. The experimental evaluation considered 6 correlation measures as baselines, 3 different image datasets, and multiple features. The results are competitive or, in most of the cases, superior to the baselines, achieving significant effectiveness gains. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:26:25Z 2021-06-25T10:26:25Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, v. 5, p. 370-378. http://hdl.handle.net/11449/206095 2-s2.0-85102977558 |
identifier_str_mv |
VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, v. 5, p. 370-378. 2-s2.0-85102977558 |
url |
http://hdl.handle.net/11449/206095 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
370-378 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128312078237696 |