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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPereira-Ferrero, Vanessa Helena-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Data de aceite: dc.date.accessioned2025-08-21T15:25:17Z-
Data de disponibilização: dc.date.available2025-08-21T15:25:17Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2022.118995-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/247785-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/247785-
Descrição: dc.descriptionImage 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.-
Descrição: dc.descriptionMicrosoft Research-
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.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Ave. 24A, 1515, São Paulo-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Ave. 24A, 1515, São Paulo-
Descrição: dc.descriptionFAPESP: #2017/25908-6-
Descrição: dc.descriptionFAPESP: #2018/15597-6-
Descrição: dc.descriptionFAPESP: #2020/02183-9-
Descrição: dc.descriptionFAPESP: #2020/11366-0-
Descrição: dc.descriptionCNPq: #309439/2020-5-
Descrição: dc.descriptionCNPq: #422667/2021-8-
Idioma: dc.languageen-
Relação: dc.relationExpert Systems with Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFeature augmentation-
Palavras-chave: dc.subjectImage classification-
Palavras-chave: dc.subjectLSTM-
Palavras-chave: dc.subjectManifold learning-
Palavras-chave: dc.subjectRanking-
Título: dc.titleFeature augmentation based on manifold ranking and LSTM for image classification[Formula presented]-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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