# Fusion of complex networks and randomized neural networks for texture analysis

arXiv: https://arxiv.org/abs/1806.09170 · HTML: https://mlsanity.com/paper/1806.09170/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps](https://mlsanity.com/paper/2303.04554/) (2023)
- [Dynamical stability and chaos in artificial neural network trajectories along training](https://mlsanity.com/paper/2404.05782/) (2024)
- [A novel weighted approach for time series forecasting based on visibility graph](https://mlsanity.com/paper/2103.13870/) (2021)
- [Deep Neural Networks as Complex Networks](https://mlsanity.com/paper/2209.05488/) (2022)
- [VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings](https://mlsanity.com/paper/2503.06368/) (2025)
- [A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata](https://mlsanity.com/paper/2211.13000/) (2022)
- [Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation](https://mlsanity.com/paper/2410.21562/) (2024)
- [MIXER: Mixed Hyperspherical Random Embedding Neural Network for Texture Recognition](https://mlsanity.com/paper/2510.03228/) (2025)

## Abstract (arXiv)

This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex networks and its topological properties as well as the image pixels are used to train randomized neural networks in order to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracies of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field.