Graph Convolutional Networks based on manifold learning for semi-supervised image classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorTemple University-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorGuimarães Pedronette, Daniel Carlos-
Autor(es): dc.creatorLatecki, Longin Jan-
Data de aceite: dc.date.accessioned2025-08-21T21:44:21Z-
Data de disponibilização: dc.date.available2025-08-21T21:44:21Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.cviu.2022.103618-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246625-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246625-
Descrição: dc.descriptionDue to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of labeled data, which is often not available, due to costs and difficulties of manual labeling processes. In this scenario, unsupervised and semi-supervised approaches have been gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a promising solution since they encode the neighborhood information and have achieved state-of-the-art results on scenarios with limited labeled data. However, since GCNs require graph-structured data, their use for semi-supervised image classification is still scarce in the literature. In this work, we propose a novel approach, the Manifold-GCN, based on GCNs for semi-supervised image classification. The main hypothesis of this paper is that the use of manifold learning to model the graph structure can further improve the GCN classification. To the best of our knowledge, this is the first framework that allows the combination of GCNs with different types of manifold learning approaches for image classification. All manifold learning algorithms employed are completely unsupervised, which is especially useful for scenarios where the availability of labeled data is a concern. A broad experimental evaluation was conducted considering 5 GCN models, 3 manifold learning approaches, 3 image datasets, and 5 deep features. The results reveal that our approach presents better accuracy than traditional and recent state-of-the-art methods with very efficient run times for both training and testing.-
Descrição: dc.descriptionFulbright Austria-
Descrição: dc.descriptionMicrosoft Research-
Descrição: dc.descriptionPetrobras-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionNational Science Foundation-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP), Av. 24-A, 1515, SP-
Descrição: dc.descriptionDepartment of Computer and Information Sciences Temple University, North 12th Street, 1925-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP), Av. 24-A, 1515, SP-
Descrição: dc.descriptionPetrobras: #2017/00285-6-
Descrição: dc.descriptionFAPESP: #2017/25908-6-
Descrição: dc.descriptionFAPESP: #2018/15597-6-
Descrição: dc.descriptionFAPESP: #2020/11366-0-
Descrição: dc.descriptionCNPq: #309439/2020-5-
Descrição: dc.descriptionCNPq: #422667/2021-8-
Descrição: dc.descriptionNational Science Foundation: IIS-2107213-
Idioma: dc.languageen-
Relação: dc.relationComputer Vision and Image Understanding-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectGraph Convolutional Networks-
Palavras-chave: dc.subjectImage classification-
Palavras-chave: dc.subjectManifold learning-
Palavras-chave: dc.subjectSemi-supervised-
Título: dc.titleGraph Convolutional Networks based on manifold learning for semi-supervised image classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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