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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.)

Spatio-spectral networks for color-texture analysis

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Autor(es):
Scabini, Leonardo F. S. [1] ; Ribas, Lucas C. [2] ; Bruno, Odemir M. [1, 2]
Número total de Autores: 3
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
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: INFORMATION SCIENCES; v. 515, p. 64-79, APR 2020.
Citações Web of Science: 0
Resumo

Texture is one of the most-studied visual attributes for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on grayscale images and discarding the color information. Therefore this work proposes a new method for color texture analysis considering all color channels in a more thorough approach. It consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel connections that cover spatial patterns of individual color channels, while between-channel connections tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through topological characterization of the modeled network in a multiscale approach with radially symmetric neighboring. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks. It also has the most stable performance between datasets, achieving 98.5(+/- 1.1) of average accuracy against 97.1(+/- 1.3) of MCND and 96.8(+/- 3.2) of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, showing that SSN presents the highest performance and robustness. (C) 2019 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 16/23763-8 - Modelagem e análise de redes complexas para visão computacional
Beneficiário:Lucas Correia Ribas
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