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

dc.contributor.authorValem, Lucas Pascotti [UNESP]
dc.contributor.authorGuimarães Pedronette, Daniel Carlos [UNESP]
dc.contributor.authorLatecki, Longin Jan
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionTemple University
dc.description.abstractDue 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.en
dc.description.affiliationDepartment of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP), Av. 24-A, 1515, SP
dc.description.affiliationDepartment of Computer and Information Sciences Temple University, North 12th Street, 1925
dc.description.affiliationUnespDepartment of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP), Av. 24-A, 1515, SP
dc.description.sponsorshipFulbright Austria
dc.description.sponsorshipMicrosoft Research
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipNational Science Foundation
dc.description.sponsorshipIdPetrobras: #2017/00285-6
dc.description.sponsorshipIdFAPESP: #2017/25908-6
dc.description.sponsorshipIdFAPESP: #2018/15597-6
dc.description.sponsorshipIdFAPESP: #2020/11366-0
dc.description.sponsorshipIdCNPq: #309439/2020-5
dc.description.sponsorshipIdCNPq: #422667/2021-8
dc.description.sponsorshipIdNational Science Foundation: IIS-2107213
dc.identifier.citationComputer Vision and Image Understanding, v. 227.
dc.relation.ispartofComputer Vision and Image Understanding
dc.subjectGraph Convolutional Networks
dc.subjectImage classification
dc.subjectManifold learning
dc.titleGraph Convolutional Networks based on manifold learning for semi-supervised image classificationen