An optimized shape descriptor based on structural properties of networks

Autor(es)

Miranda, Gisele H. B. ^[1] ; Machicao, Jeaneth ^[2] ; Bruno, Odemir M. ^{[2, 3]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

^[3] Sci Comp Grp, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: DIGITAL SIGNAL PROCESSING; v. 82, p. 216-229, NOV 2018.
• Citações Web of Science: 0

Resumo

The structural analysis of shape boundaries leads to the characterization of objects as well as to the understanding of shape properties. The literature on graphs and networks have contributed to the structural characterization of shapes with different theoretical approaches. We performed a study on the relationship between the shape architecture and the network topology constructed over the shape boundary. For that, we used a method for network modeling proposed in 2009. Firstly, together with curvature analysis, we evaluated the proposed approach for regular polygons. This way, it was possible to investigate how the network measurements vary according to some specific shape properties. Secondly, we evaluated the performance of the proposed shape descriptor in classification tasks for three datasets, accounting for both real-world and synthetic shapes. We demonstrated that not only degree related measurements are capable of distinguishing classes of objects. Yet, when using measurements that account for distinct properties of the network structure, the construction of the shape descriptor becomes more computationally efficient. Given the fact the network is dynamically constructed, the number of iterations can be reduced. The proposed approach accounts for a more robust set of structural measurements, that improved the discriminant power of the shape descriptors. (C) 2018 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 15/05899-7 - Reconhecimento de padrões em redes complexas por meio de autômatos
• Beneficiário: Gisele Helena Barboni Miranda
• Linha de fomento: Bolsas no Brasil - Doutorado
• Processo FAPESP: 16/18809-9 - Deep learning e redes complexas aplicados em visão computacional
• Beneficiário: Odemir Martinez Bruno
• Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal
• Beneficiário: Odemir Martinez Bruno
• Linha de fomento: Auxílio à Pesquisa - Regular