Show simple item record

dc.contributor.authorAfonso, Luis C. S.
dc.contributor.authorPedronette, Daniel C. G. [UNESP]
dc.contributor.authorSouza, Andre N. de [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.authorIEEE
dc.date.accessioned2019-10-05T06:23:21Z
dc.date.available2019-10-05T06:23:21Z
dc.date.issued2018-01-01
dc.identifier.citation2018 24th International Conference On Pattern Recognition (icpr). New York: Ieee, p. 560-565, 2018.
dc.identifier.issn1051-4651
dc.identifier.urihttp://hdl.handle.net/11449/186575
dc.description.abstractAppropriate metrics are paramount for machine learning and pattern recognition. In Content-based Image Retrieval-oriented applications, low-level features and pairwise-distance metrics are usually not capable of representing similarity among the objects as observed by humans. Therefore, metric learning from available data has become crucial in such applications, but just a few related approaches take into account the contextual information inherent from the samples for a better accuracy performance. In this paper, we propose a novel approach which combines an unsupervised manifold learning algorithm with the Optimum-Path Forest (OPF) classifier to obtain more accurate recognition rates, as well as we show it can outperform standard OPF-based classifiers that are trained over the original manifold. Experiments conducted in some public datasets evidenced the validity of metric learning in the context of OPF classifiers.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipPetrobras
dc.description.sponsorshipFundação para o Desenvolvimento da UNESP (FUNDUNESP)
dc.format.extent560-565
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2018 24th International Conference On Pattern Recognition (icpr)
dc.sourceWeb of Science
dc.titleImproving Optimum-Path Forest Classification Using Unsupervised Manifold Learningen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUFSCar Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil
dc.description.affiliationUNESP Sao Paulo State Univ, Inst Nat Sci & Technol, Rio Claro, Brazil
dc.description.affiliationUNESP Sao Paulo State Univ, Sch Engn, Bauru, Brazil
dc.description.affiliationUNESP Sao Paulo State Univ, Sch Sci, Bauru, Brazil
dc.description.affiliationUnespUNESP Sao Paulo State Univ, Inst Nat Sci & Technol, Rio Claro, Brazil
dc.description.affiliationUnespUNESP Sao Paulo State Univ, Sch Engn, Bauru, Brazil
dc.description.affiliationUnespUNESP Sao Paulo State Univ, Sch Sci, Bauru, Brazil
dc.identifier.wosWOS:000455146800094
dc.rights.accessRightsAcesso aberto
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2013/08645-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdFAPESP: 2017/02286-0
dc.description.sponsorshipIdFAPESP: 2017/22905-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdCNPq: 308194/2017-9
dc.description.sponsorshipIdPetrobras: 2014/00545-0
dc.description.sponsorshipIdPetrobras: 2017/00285-6
dc.description.sponsorshipIdFUNDUNESP: 2597.2017
unesp.author.lattes8212775960494686[3]
unesp.author.orcid0000-0002-8617-5404[3]
Localize o texto completo

Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record