Spatio-spectral networks for color-texture analysis

TL;DR

This paper introduces Spatio-Spectral Networks (SSN), a novel approach modeling color images as directed complex networks for color-texture analysis, outperforming existing methods including deep learning models across multiple datasets.

Contribution

The work proposes a new network-based method for color texture analysis that incorporates both spatial and spectral information, providing a more intrinsic and robust descriptor.

Findings

SSN outperforms traditional and deep learning methods in accuracy.

SSN demonstrates high stability across different datasets.

SSN shows robustness across various color spaces.

Abstract

Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this context, this work focus on a new method for color texture analysis considering all color channels in a more intrinsic approach. Our proposal consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel edges that cover spatial patterns throughout individual image color channels, while between-channel edges tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through a concise topological characterization of the modeled network in a multiscale approach with radially symmetric neighborhoods. 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, and also has the most stable performance between datasets, achieving $98.5(\pm1.1)$ of average accuracy against $97.1(\pm1.3)$ of MCND and $96.8(\pm3.2)$ of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, where results show that SSN also has higher performance and robustness.