Unsupervised similarity learning through rank correlation and kNN sets

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorDe Oliveira, Carlos Renan-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Autor(es): dc.creatorAlmeida, Jurandy-
Data de aceite: dc.date.accessioned2021-03-11T01:32:13Z-
Data de disponibilização: dc.date.available2021-03-11T01:32:13Z-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2018-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1145/3241053-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/187328-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/187328-
Descrição: dc.descriptionThe increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets.-
Idioma: dc.languageen-
Relação: dc.relationACM Transactions on Multimedia Computing, Communications and Applications-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectContent-based image retrieval-
Palavras-chave: dc.subjectKNN sets-
Palavras-chave: dc.subjectRank correlation-
Palavras-chave: dc.subjectUnsupervised learning-
Título: dc.titleUnsupervised similarity learning through rank correlation and kNN sets-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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