Learning a complex network representation for shape classification

Autor(es)

Ribas, Lucas C. ; Bruno, Odemir M.

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION; v. 154, p. 10-pg., 2024-05-18.

Resumo

Shape 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. (AU)

• Processo FAPESP: 16/23763-8 - Modelagem e análise de redes complexas para visão computacional
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 23/04583-2 - Reconhecimento de padrões em imagens baseado em redes neurais artificiais e sistemas complexos: da extração de descritores manuais ao aprendizado automático
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 21/07289-2 - Aprendizado de representações usando redes neurais artificiais e redes complexas com aplicações em sensores e biossensores
• Beneficiário: Lucas Correia Ribas
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 18/22214-6 - Rumo à convergência de tecnologias: de sensores e biossensores à visualização de informação e aprendizado de máquina para análise de dados em diagnóstico clínico
• Beneficiário: Osvaldo Novais de Oliveira Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático