Learning to classify seismic images with deep optimum-path forest
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SIBGRAPI.2016.062 http://hdl.handle.net/11449/232574 |
Resumo: | Due 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. |
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Learning to classify seismic images with deep optimum-path forestDeep RepresentationsImage ClusteringOptimum-Path ForestSeismic ImagesDue 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.Department of Computing Federal University of São CarlosInstitute of Geology University of CampinasInstitute of Computing University of CampinasDepartment of Computing São Paulo State UniversityDepartment of Computing São Paulo State UniversityUniversidade Federal de São Carlos (UFSCar)Universidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (UNESP)Afonso, LuisVidal, AlexandreKuroda, MichelleFalcao, Alexandre XavierPapa, Joao P. [UNESP]2022-04-29T22:42:10Z2022-04-29T22:42:10Z2017-01-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject401-407http://dx.doi.org/10.1109/SIBGRAPI.2016.062Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 401-407.http://hdl.handle.net/11449/23257410.1109/SIBGRAPI.2016.0622-s2.0-85013757650Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/232574Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Learning to classify seismic images with deep optimum-path forest |
title |
Learning to classify seismic images with deep optimum-path forest |
spellingShingle |
Learning to classify seismic images with deep optimum-path forest Afonso, Luis Deep Representations Image Clustering Optimum-Path Forest Seismic Images |
title_short |
Learning to classify seismic images with deep optimum-path forest |
title_full |
Learning to classify seismic images with deep optimum-path forest |
title_fullStr |
Learning to classify seismic images with deep optimum-path forest |
title_full_unstemmed |
Learning to classify seismic images with deep optimum-path forest |
title_sort |
Learning to classify seismic images with deep optimum-path forest |
author |
Afonso, Luis |
author_facet |
Afonso, Luis Vidal, Alexandre Kuroda, Michelle Falcao, Alexandre Xavier Papa, Joao P. [UNESP] |
author_role |
author |
author2 |
Vidal, Alexandre Kuroda, Michelle Falcao, Alexandre Xavier Papa, Joao P. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Afonso, Luis Vidal, Alexandre Kuroda, Michelle Falcao, Alexandre Xavier Papa, Joao P. [UNESP] |
dc.subject.por.fl_str_mv |
Deep Representations Image Clustering Optimum-Path Forest Seismic Images |
topic |
Deep Representations Image Clustering Optimum-Path Forest Seismic Images |
description |
Due 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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-10 2022-04-29T22:42:10Z 2022-04-29T22:42:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/SIBGRAPI.2016.062 Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 401-407. http://hdl.handle.net/11449/232574 10.1109/SIBGRAPI.2016.062 2-s2.0-85013757650 |
url |
http://dx.doi.org/10.1109/SIBGRAPI.2016.062 http://hdl.handle.net/11449/232574 |
identifier_str_mv |
Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 401-407. 10.1109/SIBGRAPI.2016.062 2-s2.0-85013757650 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
401-407 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1797790161187110912 |