A NOVEL RANK CORRELATION MEASURE FOR MANIFOLD LEARNING ON IMAGE RETRIEVAL AND PERSON RE-ID

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorKawai, Vinicius Atsushi Sato-
Autor(es): dc.creatorPereira-Ferrero, Vanessa Helena-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Data de aceite: dc.date.accessioned2025-08-21T22:35:45Z-
Data de disponibilização: dc.date.available2025-08-21T22:35:45Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICIP46576.2022.9898060-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248249-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248249-
Descrição: dc.descriptionEffectively measuring similarity among data samples represented as points in high-dimensional spaces remains a major challenge in retrieval, machine learning, and computer vision. In these scenarios, unsupervised manifold learning techniques grounded on rank information have been demonstrated to be a promising solution. However, various methods rely on rank correlation measures, which often depend on a proper definition of neighborhood size. On current approaches, this definition may lead to a reduction in the final desired effectiveness. In this work, a novel rank correlation measure robust to such variations is proposed for manifold learning approaches. The proposed measure is suitable for diverse scenarios and is validated on a Manifold Learning Algorithm based on Correlation Graph (CG). The experimental evaluation considered 6 datasets on general image retrieval and person Re-ID, achieving results superior to most state-of-the-art methods.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
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)-
Formato: dc.format1371-1375-
Idioma: dc.languageen-
Relação: dc.relationProceedings - International Conference on Image Processing, ICIP-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcorrelation graph-
Palavras-chave: dc.subjectimage retrieval-
Palavras-chave: dc.subjectmanifold learning-
Palavras-chave: dc.subjectperson Re-ID-
Palavras-chave: dc.subjectrank correlation measures-
Título: dc.titleA NOVEL RANK CORRELATION MEASURE FOR MANIFOLD LEARNING ON IMAGE RETRIEVAL AND PERSON RE-ID-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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