Feature augmentation based on manifold ranking and LSTM for image classification[Formula presented]

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dc.contributor.authorPereira-Ferrero, Vanessa Helena [UNESP]
dc.contributor.authorValem, Lucas Pascotti [UNESP]
dc.contributor.authorPedronette, Daniel Carlos Guimarães [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T13:25:50Z
dc.date.available2023-07-29T13:25:50Z
dc.date.issued2023-03-01
dc.description.abstractImage classification is a critical topic due to its wide application and several challenges associated. Despite the huge progress made last decades, there is still a demand for context-aware image representation approaches capable of taking into the dataset manifold for improving classification accuracy. In this work, a representation learning approach is proposed, based on a novel feature augmentation strategy. The proposed method aims to exploit available contextual similarity information through rank-based manifold learning used to define and assign weights to samples used in augmentation. The approach is validated using CNN-based features and LSTM models to achieve even higher accuracy results on image classification tasks. Experimental results show that the feature augmentation strategy can indeed improve the accuracy of results on widely used image datasets (CIFAR10, Stanford Dogs, Linnaeus5, Flowers102 and Flowers17) in different CNNs (ResNet152, VGG16, DPN92). The results indicate gains up to 20% and show the potential of the developed approach in achieving higher accuracy results for image classification.en
dc.description.affiliationDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Ave. 24A, 1515, São Paulo
dc.description.affiliationUnespDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Ave. 24A, 1515, São Paulo
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.sponsorshipIdFAPESP: #2017/25908-6
dc.description.sponsorshipIdFAPESP: #2018/15597-6
dc.description.sponsorshipIdFAPESP: #2020/02183-9
dc.description.sponsorshipIdFAPESP: #2020/11366-0
dc.description.sponsorshipIdCNPq: #309439/2020-5
dc.description.sponsorshipIdCNPq: #422667/2021-8
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2022.118995
dc.identifier.citationExpert Systems with Applications, v. 213.
dc.identifier.doi10.1016/j.eswa.2022.118995
dc.identifier.issn0957-4174
dc.identifier.scopus2-s2.0-85140439236
dc.identifier.urihttp://hdl.handle.net/11449/247785
dc.language.isoeng
dc.relation.ispartofExpert Systems with Applications
dc.sourceScopus
dc.subjectFeature augmentation
dc.subjectImage classification
dc.subjectLSTM
dc.subjectManifold learning
dc.subjectRanking
dc.titleFeature augmentation based on manifold ranking and LSTM for image classification[Formula presented]en
dc.typeArtigo
unesp.author.orcid0000-0003-1363-5649[1]
unesp.author.orcid0000-0002-2867-4838[3]

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE