Fusion of complex networks and randomized neural networks for texture analysis

Author(s)

Ribas, Lucas C. ^{[1, 2]} ; Sa Junior, Jarbas Joaci de Mesquita ^[3] ; Scabini, Leonardo F. S. ^[2] ; Bruno, Odemir M. ^{[1, 2]}

Affiliation

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

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

^[3] Univ Fed Ceara, Curso Engn Computacao, Programa Posgrad Engn Eletr & Computacao, Campus Sobral, Rua Coronel Estanislau Frota 563, Sobral 62010560, CE - Brazil

• Document type: Journal article
• Source: PATTERN RECOGNITION; v. 103, JUL 2020.
• Web of Science Citations: 0

Abstract

This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex network and its topological properties as well as the image pixels are used to train randomized neural networks to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracy of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field. (C) 2019 Published by Elsevier Ltd. (AU)

• FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
• Grantee: Lucas Correia Ribas
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 16/18809-9 - Deep learning and complex networks applied to computer vision
• Grantee: Odemir Martinez Bruno
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
• Grantee: Odemir Martinez Bruno
• Support Opportunities: Regular Research Grants