A Multi-level Rank Correlation Measure for Image Retrieval

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSa, Nikolas Gomes de [UNESP]-
Autor(es): dc.creatorValem, Lucas Pascotti [UNESP]-
Autor(es): dc.creatorGuimaraes Pedronette, Daniel Carlos [UNESP]-
Autor(es): dc.creatorFarinella, G. M.-
Autor(es): dc.creatorRadeva, P.-
Autor(es): dc.creatorBraz, J.-
Autor(es): dc.creatorBouatouch, K.-
Data de aceite: dc.date.accessioned2022-08-04T21:58:38Z-
Data de disponibilização: dc.date.available2022-08-04T21:58:38Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0010220903700378-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/218606-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/218606-
Descrição: dc.descriptionAccurately 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionMicrosoft Research-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Dept Stat Appl Math & Comp, Rio Claro, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, UNESP, Dept Stat Appl Math & Comp, Rio Claro, Brazil-
Descrição: dc.descriptionFAPESP: 2018/15597-6-
Descrição: dc.descriptionFAPESP: 2017/25908-6-
Descrição: dc.descriptionFAPESP: 2019/11104-8-
Descrição: dc.descriptionFAPESP: 2020/113660-
Descrição: dc.descriptionCNPq: 308194/2017-9-
Formato: dc.format370-378-
Idioma: dc.languageen-
Publicador: dc.publisherScitepress-
Relação: dc.relationVisapp: Proceedings Of The 16th International Joint Conference On Computer Vision, Imaging And Computer Graphics Theory And Applications - Vol. 5: Visapp-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectContent-based Image Retrieval-
Palavras-chave: dc.subjectRank Correlation-
Palavras-chave: dc.subjectUnsupervised Learning-
Palavras-chave: dc.subjectInformation Retrieval-
Título: dc.titleA Multi-level Rank Correlation Measure for Image Retrieval-
Aparece nas coleções:Repositório Institucional - Unesp

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