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

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dc.contributor.authorValem, Lucas Pascotti [UNESP]
dc.contributor.authorKawai, Vinicius Atsushi Sato [UNESP]
dc.contributor.authorPereira-Ferrero, Vanessa Helena [UNESP]
dc.contributor.authorPedronette, Daniel Carlos Guimarães [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T13:38:43Z
dc.date.available2023-07-29T13:38:43Z
dc.date.issued2022-01-01
dc.description.abstractEffectively 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.en
dc.description.affiliationDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)
dc.description.affiliationUnespDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.format.extent1371-1375
dc.identifierhttp://dx.doi.org/10.1109/ICIP46576.2022.9898060
dc.identifier.citationProceedings - International Conference on Image Processing, ICIP, p. 1371-1375.
dc.identifier.doi10.1109/ICIP46576.2022.9898060
dc.identifier.issn1522-4880
dc.identifier.scopus2-s2.0-85146728543
dc.identifier.urihttp://hdl.handle.net/11449/248249
dc.language.isoeng
dc.relation.ispartofProceedings - International Conference on Image Processing, ICIP
dc.sourceScopus
dc.subjectcorrelation graph
dc.subjectimage retrieval
dc.subjectmanifold learning
dc.subjectperson Re-ID
dc.subjectrank correlation measures
dc.titleA NOVEL RANK CORRELATION MEASURE FOR MANIFOLD LEARNING ON IMAGE RETRIEVAL AND PERSON RE-IDen
dc.typeTrabalho apresentado em evento

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE