Multilayer complex network descriptors for color-texture characterization

Author(s)

Scabini, Leonardo F. S. ^[1] ; Condori, Rayner H. M. ^[2] ; Goncalves, Wesley N. ^[3] ; Bruno, Odemir M. ^{[1, 2]}

Affiliation

^[1] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[3] Univ Fed Mato Grosso do Sul, Ponta Pora, MS - Brazil

• Document type: Journal article
• Source: INFORMATION SCIENCES; v. 491, p. 30-47, JUL 2019.
• Web of Science Citations: 2

Abstract

A new method based on complex networks is proposed for color-texture analysis. The proposal consists of modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outexl3, CURet, and MBT. Results among various literature methods are compared, including deep convolutional neural networks. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers. (C) 2019 Published by Elsevier Inc. (AU)

• FAPESP's process: 16/18809-9 - Deep learning and complex networks applied to computer vision
• Grantee: Odemir Martinez Bruno
• Support type: 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 type: Regular Research Grants