  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.
