Interactive image segmentation of non-contiguous classes using particle competition and cooperation

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dc.contributor.authorBreve, Fabricio [UNESP]
dc.contributor.authorQuiles, Marcos G.
dc.contributor.authorZhao, Liang
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2018-12-11T16:40:06Z
dc.date.available2018-12-11T16:40:06Z
dc.date.issued2015-01-01
dc.description.abstractSemi-supervised learning methods employ both labeled and unlabeled data in their training process. Therefore, they are commonly applied to interactive image processing tasks, where a human specialist may label a few pixels from the image and the algorithm would automatically propagate them to the remaining pixels, classifying the entire image. The particle competition and cooperation model is a recently proposed graph-based model, which was developed to perform semi-supervised classification. It employs teams of particles walking in a undirected and unweighed graph in order to classify data items corresponding to graph nodes. Each team represents a class problem, they try to dominate the unlabeled nodes in their neighborhood, at the same time that they try to avoid invasion from other teams. In this paper, the particle competition and cooperation model is applied to the task of interactive image segmentation. Image pixels are converted to graph nodes. Nodes are connected if they represent pixels with visual similarities. Labeled pixels generate particles that propagate their labels to the unlabeled pixels. Computer simulations are performed on some real-world images to show the effectiveness of the proposed approach. Images are correctly segmented in regions of interest, including non-contiguous regions.en
dc.description.affiliationSão Paulo State University (UNESP)
dc.description.affiliationFederal University of São Paulo (Unifesp)
dc.description.affiliationUniversity of São Paulo (USP)
dc.description.affiliationUnespSão Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.format.extent203-216
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-21404-7_15
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9155, p. 203-216.
dc.identifier.doi10.1007/978-3-319-21404-7_15
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-84949032454
dc.identifier.urihttp://hdl.handle.net/11449/168178
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectInteractive image segmentation
dc.subjectMachine learning
dc.subjectParticle competition and cooperation
dc.subjectSemi-supervised learning
dc.titleInteractive image segmentation of non-contiguous classes using particle competition and cooperationen
dc.typeTrabalho apresentado em evento

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