| Full text | |
| Author(s): |
Total Authors: 3
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| 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
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | INFORMATION SCIENCES; v. 515, p. 64-79, APR 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| 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 |