Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach

Detalhes bibliográficos
Autor(a) principal: Passerini, Jefferson Antonio Ribeiro [UNESP]
Data de Publicação: 2020
Outros Autores: Breve, Fabricio [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-030-58799-4_67
http://hdl.handle.net/11449/221587
Resumo: In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist’s intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49% when classifying pixels with the proposed model while the reference model had an error rate of 3.14%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.
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spelling Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New ApproachInteractive image segmentationMachine learningParticle competition and cooperationSemi-supervised learningIn the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist’s intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49% when classifying pixels with the proposed model while the reference model had an error rate of 3.14%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)São Paulo State UniversitySão Paulo State UniversityFAPESP: #2016/05669-4Universidade Estadual Paulista (UNESP)Passerini, Jefferson Antonio Ribeiro [UNESP]Breve, Fabricio [UNESP]2022-04-28T19:29:28Z2022-04-28T19:29:28Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject935-950http://dx.doi.org/10.1007/978-3-030-58799-4_67Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12249 LNCS, p. 935-950.1611-33490302-9743http://hdl.handle.net/11449/22158710.1007/978-3-030-58799-4_672-s2.0-85092705249Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2022-04-28T19:29:28Zoai:repositorio.unesp.br:11449/221587Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:31:27.033910Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
title Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
spellingShingle Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
Passerini, Jefferson Antonio Ribeiro [UNESP]
Interactive image segmentation
Machine learning
Particle competition and cooperation
Semi-supervised learning
title_short Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
title_full Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
title_fullStr Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
title_full_unstemmed Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
title_sort Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach
author Passerini, Jefferson Antonio Ribeiro [UNESP]
author_facet Passerini, Jefferson Antonio Ribeiro [UNESP]
Breve, Fabricio [UNESP]
author_role author
author2 Breve, Fabricio [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Passerini, Jefferson Antonio Ribeiro [UNESP]
Breve, Fabricio [UNESP]
dc.subject.por.fl_str_mv Interactive image segmentation
Machine learning
Particle competition and cooperation
Semi-supervised learning
topic Interactive image segmentation
Machine learning
Particle competition and cooperation
Semi-supervised learning
description In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist’s intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49% when classifying pixels with the proposed model while the reference model had an error rate of 3.14%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2022-04-28T19:29:28Z
2022-04-28T19:29:28Z
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/10.1007/978-3-030-58799-4_67
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12249 LNCS, p. 935-950.
1611-3349
0302-9743
http://hdl.handle.net/11449/221587
10.1007/978-3-030-58799-4_67
2-s2.0-85092705249
url http://dx.doi.org/10.1007/978-3-030-58799-4_67
http://hdl.handle.net/11449/221587
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12249 LNCS, p. 935-950.
1611-3349
0302-9743
10.1007/978-3-030-58799-4_67
2-s2.0-85092705249
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 935-950
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
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