# Multilayer Complex Network Descriptors for Color-Texture Characterization

arXiv: https://arxiv.org/abs/1804.00501 · HTML: https://mlsanity.com/paper/1804.00501/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps](https://mlsanity.com/paper/2303.04554/) (2023)
- [A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis](https://mlsanity.com/paper/2406.06136/) (2024)
- [MIXER: Mixed Hyperspherical Random Embedding Neural Network for Texture Recognition](https://mlsanity.com/paper/2510.03228/) (2025)

## Abstract (arXiv)

A new method based on complex networks is proposed for color-texture analysis. The proposal consists on 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, Outex13, CURet and MBT. Results among various literature methods are compared, including deep convolutional neural networks with pre-trained architectures. 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.