Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6806052&tag=1 http://hdl.handle.net/11449/130056 |
Resumo: | Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance. |
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Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cutImage segmentationWatershed transformGraph partitioningNormalized cutUnsupervised distance learningResearch on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.School of Technology, UNICAMP, Limeira, São Paulo, Brazil.Universidade Estadual Paulista, Department of Statistics, Applied Mathematics and Computing, BR-13506900 São Paulo, BrazilIeeeUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Pinto, Tiago W.Carvalho, Marco A. G. dePedronette, Daniel C. G. [UNESP]Martins, Paulo S.IEEE2015-11-03T15:28:55Z2015-11-03T15:28:55Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject153-156http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6806052&tag=12014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014). New York: Ieee, p. 153-156, 2014.1550-5782http://hdl.handle.net/11449/130056WOS:000355255900038Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014)info:eu-repo/semantics/openAccess2021-10-23T21:37:44Zoai:repositorio.unesp.br:11449/130056Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:37:44Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
title |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
spellingShingle |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut Pinto, Tiago W. Image segmentation Watershed transform Graph partitioning Normalized cut Unsupervised distance learning |
title_short |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
title_full |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
title_fullStr |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
title_full_unstemmed |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
title_sort |
Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut |
author |
Pinto, Tiago W. |
author_facet |
Pinto, Tiago W. Carvalho, Marco A. G. de Pedronette, Daniel C. G. [UNESP] Martins, Paulo S. IEEE |
author_role |
author |
author2 |
Carvalho, Marco A. G. de Pedronette, Daniel C. G. [UNESP] Martins, Paulo S. IEEE |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Pinto, Tiago W. Carvalho, Marco A. G. de Pedronette, Daniel C. G. [UNESP] Martins, Paulo S. IEEE |
dc.subject.por.fl_str_mv |
Image segmentation Watershed transform Graph partitioning Normalized cut Unsupervised distance learning |
topic |
Image segmentation Watershed transform Graph partitioning Normalized cut Unsupervised distance learning |
description |
Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2015-11-03T15:28:55Z 2015-11-03T15:28:55Z |
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://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6806052&tag=1 2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014). New York: Ieee, p. 153-156, 2014. 1550-5782 http://hdl.handle.net/11449/130056 WOS:000355255900038 |
url |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6806052&tag=1 http://hdl.handle.net/11449/130056 |
identifier_str_mv |
2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014). New York: Ieee, p. 153-156, 2014. 1550-5782 WOS:000355255900038 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
153-156 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1803046852882333696 |