Learning to classify seismic images with deep optimum-path forest

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAfonso, Luis-
Autor(es): dc.creatorVidal, Alexandre-
Autor(es): dc.creatorKuroda, Michelle-
Autor(es): dc.creatorFalcao, Alexandre Xavier-
Autor(es): dc.creatorPapa, Joao P.-
Data de aceite: dc.date.accessioned2025-08-21T21:35:41Z-
Data de disponibilização: dc.date.available2025-08-21T21:35:41Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2017-01-10-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI.2016.062-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/232574-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/232574-
Descrição: dc.descriptionDue to the lack of labeled information, clustering techniques have been paramount in the last years once more. In this paper, inspired by the deep learning phenomenon, we presented a multi-scale approach to obtain more refined cluster representations of the Optimum-Path Forest (OPF) classifier, which has obtained promising results in a number of works in the literature. Here, we propose to fill a gap in OPF-based works by using a deep-driven representation of the feature space. Additionally, we validated the work in the context of high resolution seismic images aiming at petroleum exploration, as well as in general-purpose applications. Quantitative and qualitative analysis are conducted in order to assess the robustness of the proposed approach.-
Descrição: dc.descriptionDepartment of Computing Federal University of São Carlos-
Descrição: dc.descriptionInstitute of Geology University of Campinas-
Descrição: dc.descriptionInstitute of Computing University of Campinas-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format401-407-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep Representations-
Palavras-chave: dc.subjectImage Clustering-
Palavras-chave: dc.subjectOptimum-Path Forest-
Palavras-chave: dc.subjectSeismic Images-
Título: dc.titleLearning to classify seismic images with deep optimum-path forest-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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