A BFS-Tree of ranking references for unsupervised manifold learning

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorNTNU - Norwegian University of Science and Technology-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães [UNESP]-
Autor(es): dc.creatorValem, Lucas Pascotti [UNESP]-
Autor(es): dc.creatorTorres, Ricardo da S.-
Data de aceite: dc.date.accessioned2022-02-22T00:47:40Z-
Data de disponibilização: dc.date.available2022-02-22T00:47:40Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2020.107666-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/206629-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/206629-
Descrição: dc.descriptionContextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches.-
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.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: #2014/12236-1-
Descrição: dc.descriptionFAPESP: #2015/24494-8-
Descrição: dc.descriptionFAPESP: #2016/50250-1-
Descrição: dc.descriptionFAPESP: #2017/20945-0-
Descrição: dc.descriptionFAPESP: #2017/25908-6-
Descrição: dc.descriptionFAPESP: #2018/15597-6-
Descrição: dc.descriptionCNPq: #307560/2016-3-
Descrição: dc.descriptionCNPq: #308194/2017-9-
Descrição: dc.descriptionCAPES: #88881.145912/2017-01-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectContent-based image retrieval-
Palavras-chave: dc.subjectRanking references-
Palavras-chave: dc.subjectTree representation-
Palavras-chave: dc.subjectUnsupervised manifold learning-
Título: dc.titleA BFS-Tree of ranking references for unsupervised manifold learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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