Learning a complex network representation for shape classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorRibas, Lucas C.-
Autor(es): dc.creatorBruno, Odemir M.-
Data de aceite: dc.date.accessioned2025-08-21T16:15:03Z-
Data de disponibilização: dc.date.available2025-08-21T16:15:03Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2024.110566-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/299266-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/299266-
Descrição: dc.descriptionShape contour is a key low-level characteristic, making shape description an important aspect in many computer vision problems, with several challenges such as variations in scale, rotation, and noise. In this paper, we introduce an approach for shape analysis and classification from binary images based on representations learned by applying Randomized Neural Networks (RNNs) on feature maps derived from a Complex Network (CN) framework. Our approach models the contour in a complex network and computes their topological measures using a dynamic evolution strategy. This evolution of the CN provides significant information into the physical aspects of the shape's contour. Therefore, we propose embedding the topological measures computed from the dynamics of the CN evolution into a matrix representation, which we have named the Topological Feature Map (TFM). Then, we employ the RNN to learn representations from the TFM through a sliding window strategy. The proposed representation is formed by the learned weights between the hidden and output layers of the RNN. Our experimental results show performance improvements in shape classification using the proposed method across two generic shape datasets. We also applied our approach to the recognition of plant leaves, achieving high performance in this challenging task. Furthermore, the proposed approach has demonstrated robustness to noise and invariance to transformations in scale and orientation of the shapes.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSão Paulo State University Institute of Biosciences Humanities and Exact Sciences, Rua Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionSão Carlos Institute of Physics University of São Paulo, PO Box 369, SP-
Descrição: dc.descriptionSão Paulo State University Institute of Biosciences Humanities and Exact Sciences, Rua Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionCNPq: # 307897/2018-4-
Descrição: dc.descriptionFAPESP: #s 2016/23763-8-
Descrição: dc.descriptionFAPESP: 2018/22214-6-
Descrição: dc.descriptionFAPESP: 2021/07289-2-
Descrição: dc.descriptionFAPESP: 2023/04583-2-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComplex network-
Palavras-chave: dc.subjectNeural network-
Palavras-chave: dc.subjectShape representation-
Título: dc.titleLearning a complex network representation for shape classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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