Improving Optimum-Path Forest Classification Using Unsupervised Manifold Learning
dc.contributor.author | Afonso, Luis C. S. | |
dc.contributor.author | Pedronette, Daniel C. G. [UNESP] | |
dc.contributor.author | Souza, Andre N. de [UNESP] | |
dc.contributor.author | Papa, Joao P. [UNESP] | |
dc.contributor.author | IEEE | |
dc.contributor.institution | Universidade Federal de São Carlos (UFSCar) | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2019-10-05T06:23:21Z | |
dc.date.available | 2019-10-05T06:23:21Z | |
dc.date.issued | 2018-01-01 | |
dc.description.abstract | Appropriate 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.affiliation | UFSCar Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil | |
dc.description.affiliation | UNESP Sao Paulo State Univ, Inst Nat Sci & Technol, Rio Claro, Brazil | |
dc.description.affiliation | UNESP Sao Paulo State Univ, Sch Engn, Bauru, Brazil | |
dc.description.affiliation | UNESP Sao Paulo State Univ, Sch Sci, Bauru, Brazil | |
dc.description.affiliationUnesp | UNESP Sao Paulo State Univ, Inst Nat Sci & Technol, Rio Claro, Brazil | |
dc.description.affiliationUnesp | UNESP Sao Paulo State Univ, Sch Engn, Bauru, Brazil | |
dc.description.affiliationUnesp | UNESP Sao Paulo State Univ, Sch Sci, Bauru, Brazil | |
dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
dc.description.sponsorship | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) | |
dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
dc.description.sponsorship | Petrobras | |
dc.description.sponsorship | Fundação para o Desenvolvimento da UNESP (FUNDUNESP) | |
dc.description.sponsorshipId | FAPESP: 2013/07375-0 | |
dc.description.sponsorshipId | FAPESP: 2013/08645-0 | |
dc.description.sponsorshipId | FAPESP: 2014/12236-1 | |
dc.description.sponsorshipId | FAPESP: 2016/19403-6 | |
dc.description.sponsorshipId | FAPESP: 2017/02286-0 | |
dc.description.sponsorshipId | FAPESP: 2017/22905-6 | |
dc.description.sponsorshipId | CNPq: 306166/2014-3 | |
dc.description.sponsorshipId | CNPq: 307066/2017-7 | |
dc.description.sponsorshipId | CNPq: 308194/2017-9 | |
dc.description.sponsorshipId | Petrobras: 2014/00545-0 | |
dc.description.sponsorshipId | Petrobras: 2017/00285-6 | |
dc.description.sponsorshipId | FUNDUNESP: 2597.2017 | |
dc.format.extent | 560-565 | |
dc.identifier.citation | 2018 24th International Conference On Pattern Recognition (icpr). New York: Ieee, p. 560-565, 2018. | |
dc.identifier.issn | 1051-4651 | |
dc.identifier.uri | http://hdl.handle.net/11449/186575 | |
dc.identifier.wos | WOS:000455146800094 | |
dc.language.iso | eng | |
dc.publisher | Ieee | |
dc.relation.ispartof | 2018 24th International Conference On Pattern Recognition (icpr) | |
dc.rights.accessRights | Acesso aberto | |
dc.source | Web of Science | |
dc.title | Improving Optimum-Path Forest Classification Using Unsupervised Manifold Learning | en |
dc.type | Trabalho apresentado em evento | |
dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
dcterms.rightsHolder | Ieee | |
unesp.author.lattes | 8212775960494686[3] | |
unesp.author.orcid | 0000-0002-8617-5404[3] | |
unesp.campus | Universidade Estadual Paulista (Unesp), Faculdade de Ciências, Bauru | pt |
unesp.department | Computação - FC | pt |