Interactive Image Segmentation using Particle Competition and Cooperation
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/161235 |
Resumo: | Many interactive image processing approaches are based on semi-supervised learning, which employ both labeled and unlabeled data in its training process. In the interactive image segmentation problem, a human specialist labels some pixels of an object while the semi-supervised algorithm labels the remaining pixels of the segment. The particle competition and cooperation model is a recent graph-based semi-supervised learning approach. It employs particles walking in a graph to classify the data items corresponding to graph nodes. Each particle group aims to dominate most unlabeled nodes, spreading their label, and preventing enemy particles invasion. In this paper, the particle competition and cooperation model is extended to perform interactive image segmentation. Each image pixel is converted into a graph node, which is connected to its nearest neighbors according to their visual features and location in the original image. Labeled pixel generates particles that propagate their label to the unlabeled pixels. The particle model also takes the contributions from the adjacent pixels to classify less confident labeled pixels. Computer simulations are performed on real-world images, including images from the Microsoft GrabCut dataset, which allows a straightly comparison with other techniques. The segmentation results show the effectiveness of the proposed approach. |
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Repositório Institucional da UNESP |
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Interactive Image Segmentation using Particle Competition and CooperationMany interactive image processing approaches are based on semi-supervised learning, which employ both labeled and unlabeled data in its training process. In the interactive image segmentation problem, a human specialist labels some pixels of an object while the semi-supervised algorithm labels the remaining pixels of the segment. The particle competition and cooperation model is a recent graph-based semi-supervised learning approach. It employs particles walking in a graph to classify the data items corresponding to graph nodes. Each particle group aims to dominate most unlabeled nodes, spreading their label, and preventing enemy particles invasion. In this paper, the particle competition and cooperation model is extended to perform interactive image segmentation. Each image pixel is converted into a graph node, which is connected to its nearest neighbors according to their visual features and location in the original image. Labeled pixel generates particles that propagate their label to the unlabeled pixels. The particle model also takes the contributions from the adjacent pixels to classify less confident labeled pixels. Computer simulations are performed on real-world images, including images from the Microsoft GrabCut dataset, which allows a straightly comparison with other techniques. The segmentation results show the effectiveness of the proposed approach.Sao Paulo State Univ UNESP, Inst Geosci & Exact Sci IGCE, Dept Stat Appl Math & Computat DEMAC, Sao Paulo, BrazilFed Univ Sao Paulo Unifesp, Inst Sci & Technol ICT, Sao Jose Dos Campos, SP, BrazilUniv Sao Paulo, Sch Philosophy Sci & Literature Ribeirao Preto FF, Dept Comp Sci & Math DCM, Sao Paulo, BrazilSao Paulo State Univ UNESP, Inst Geosci & Exact Sci IGCE, Dept Stat Appl Math & Computat DEMAC, Sao Paulo, BrazilIeeeUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Breve, Fabricio [UNESP]Quiles, Marcos GoncalvesZhao, LiangIEEE2018-11-26T16:26:29Z2018-11-26T16:26:29Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject8application/pdf2015 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 8 p., 2015.2161-4393http://hdl.handle.net/11449/161235WOS:000370730602005WOS000370730602005.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 International Joint Conference On Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2024-01-18T06:24:14Zoai:repositorio.unesp.br:11449/161235Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:19:29.055268Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Interactive Image Segmentation using Particle Competition and Cooperation |
title |
Interactive Image Segmentation using Particle Competition and Cooperation |
spellingShingle |
Interactive Image Segmentation using Particle Competition and Cooperation Breve, Fabricio [UNESP] |
title_short |
Interactive Image Segmentation using Particle Competition and Cooperation |
title_full |
Interactive Image Segmentation using Particle Competition and Cooperation |
title_fullStr |
Interactive Image Segmentation using Particle Competition and Cooperation |
title_full_unstemmed |
Interactive Image Segmentation using Particle Competition and Cooperation |
title_sort |
Interactive Image Segmentation using Particle Competition and Cooperation |
author |
Breve, Fabricio [UNESP] |
author_facet |
Breve, Fabricio [UNESP] Quiles, Marcos Goncalves Zhao, Liang IEEE |
author_role |
author |
author2 |
Quiles, Marcos Goncalves Zhao, Liang IEEE |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Breve, Fabricio [UNESP] Quiles, Marcos Goncalves Zhao, Liang IEEE |
description |
Many interactive image processing approaches are based on semi-supervised learning, which employ both labeled and unlabeled data in its training process. In the interactive image segmentation problem, a human specialist labels some pixels of an object while the semi-supervised algorithm labels the remaining pixels of the segment. The particle competition and cooperation model is a recent graph-based semi-supervised learning approach. It employs particles walking in a graph to classify the data items corresponding to graph nodes. Each particle group aims to dominate most unlabeled nodes, spreading their label, and preventing enemy particles invasion. In this paper, the particle competition and cooperation model is extended to perform interactive image segmentation. Each image pixel is converted into a graph node, which is connected to its nearest neighbors according to their visual features and location in the original image. Labeled pixel generates particles that propagate their label to the unlabeled pixels. The particle model also takes the contributions from the adjacent pixels to classify less confident labeled pixels. Computer simulations are performed on real-world images, including images from the Microsoft GrabCut dataset, which allows a straightly comparison with other techniques. The segmentation results show the effectiveness of the proposed approach. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01 2018-11-26T16:26:29Z 2018-11-26T16:26:29Z |
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 |
2015 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 8 p., 2015. 2161-4393 http://hdl.handle.net/11449/161235 WOS:000370730602005 WOS000370730602005.pdf |
identifier_str_mv |
2015 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 8 p., 2015. 2161-4393 WOS:000370730602005 WOS000370730602005.pdf |
url |
http://hdl.handle.net/11449/161235 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2015 International Joint Conference On Neural Networks (ijcnn) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
8 application/pdf |
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_ |
1808129507517792256 |