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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Multilayer complex network descriptors for color-texture characterization

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Autor(es):
Scabini, Leonardo F. S. [1] ; Condori, Rayner H. M. [2] ; Goncalves, Wesley N. [3] ; Bruno, Odemir M. [1, 2]
Número total de Autores: 4
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: INFORMATION SCIENCES; v. 491, p. 30-47, JUL 2019.
Citações Web of Science: 2
Resumo

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)

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