Graph-based selective rank fusion for unsupervised image retrieval

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorValem, Lucas Pascotti [UNESP]-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:25:43Z-
Data de disponibilização: dc.date.available2022-02-22T00:25:43Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patrec.2020.03.032-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198844-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198844-
Descrição: dc.descriptionNowadays, there is a great variety of visual features available for image retrieval tasks. While fusion strategies have been established as a promising alternative, an inherent difficulty in unsupervised scenarios is the task of selecting the features to combine. In this paper, a Graph-based Selective Rank Fusion is proposed. The graph is used to represent the effectiveness estimation of features and the complementarity among them. The selected combinations are defined by the Connected Components of the graph. High-effective retrieval results were achieved through a comprehensive experimental evaluation considering different public datasets, dozens of features and comparisons with related methods. Relative gains up to +54.73% were obtained in relation to the best isolated feature.-
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.descriptionDepartment of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP), Av. 24-A, 1515-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP), Av. 24-A, 1515-
Descrição: dc.descriptionFAPESP: #2013/08645-0-
Descrição: dc.descriptionFAPESP: #2017/02091-4-
Descrição: dc.descriptionFAPESP: #2017/25908-6-
Descrição: dc.descriptionFAPESP: #2018/15597-6-
Descrição: dc.descriptionCNPq: #308194/2017-9-
Formato: dc.format82-89-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition Letters-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectContent-based image retrieval-
Palavras-chave: dc.subjectCorrelation measure-
Palavras-chave: dc.subjectEffectiveness estimation-
Palavras-chave: dc.subjectRank-aggregation-
Palavras-chave: dc.subjectUnsupervised late fusion-
Título: dc.titleGraph-based selective rank fusion for unsupervised image retrieval-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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