Feature Fusion and Augmentation Based on Manifold Ranking for Image Classification

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.creatorLeticio, Gustavo Rosseto-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Data de aceite: dc.date.accessioned2025-08-21T18:33:03Z-
Data de disponibilização: dc.date.available2025-08-21T18:33:03Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1142/S1793351X24440033-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308229-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308229-
Descrição: dc.descriptionDespite the great advances in the field of image classification, the association of ideal approaches that can bring improved results, considering different datasets, is still an open challenge. In this work, a novel approach is presented, based on a combination of compared strategies: feature extraction for early fusion; rankings based on manifold learning for late fusion; and feature augmentation applied in a long short-term memory (LSTM) algorithm. The proposed method aims to investigate the effect of feature fusion (early fusion) and ranking fusion (late fusion) in the final results of image classification. The experimental results showed that the proposed strategies improved the accuracy of results in different tested datasets (such as CIFAR10, Stanford Dogs, Linnaeus 5, Flowers 102, and Flowers 17) using a fusion of features from three convolutional neural networks (CNNs) (ResNet152, VGG16, and DPN92) and its respective generated rankings. The results indicated significant improvements and showed the potential of the approach proposed for image classification.-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Rio Claro-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Rio Claro-
Formato: dc.format591-612-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Semantic Computing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectearly fusion-
Palavras-chave: dc.subjectfeature fusion-
Palavras-chave: dc.subjectImage classification-
Palavras-chave: dc.subjectlate fusion-
Palavras-chave: dc.subjectLSTM-
Palavras-chave: dc.subjectmanifold learning-
Palavras-chave: dc.subjectranking-
Título: dc.titleFeature Fusion and Augmentation Based on Manifold Ranking for Image Classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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