Person Re-ID through unsupervised hypergraph rank selection and fusion

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Data de aceite: dc.date.accessioned2025-08-21T17:09:14Z-
Data de disponibilização: dc.date.available2025-08-21T17:09:14Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.imavis.2022.104473-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241114-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241114-
Descrição: dc.descriptionPerson Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals across multiple cameras that have no overlapping views. Most of the approaches require labeled data, which is not always available, given the huge amount of demanded data and the difficulty of manually assigning a class for each individual. Recently, studies have shown that re-ranking methods are capable of achieving significant gains, especially in the absence of labeled data. Besides that, the fusion of feature extractors and multiple-source training is another promising research direction not extensively exploited. We aim to fill this gap through a manifold rank aggregation approach capable of exploiting the complementarity of different person Re-ID rankers. In this work, we perform a completely unsupervised selection and fusion of diverse ranked lists obtained from multiple and diverse feature extractors. Among the contributions, this work proposes a query performance prediction measure that models the relationship among images considering a hypergraph structure and does not require the use of any labeled data. Expressive gains were obtained in four datasets commonly used for person Re-ID. We achieved results competitive to the state-of-the-art in most of the scenarios.-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationImage and Vision Computing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFusion-
Palavras-chave: dc.subjectHypergraph-
Palavras-chave: dc.subjectPerson Re-ID-
Palavras-chave: dc.subjectRank-
Palavras-chave: dc.subjectSelection-
Palavras-chave: dc.subjectUnsupervised-
Título: dc.titlePerson Re-ID through unsupervised hypergraph rank selection and fusion-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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