Fusion of complex networks and randomized neural networks for texture analysis

TL;DR

This paper introduces a novel texture analysis method combining complex network modeling and randomized neural networks, achieving superior accuracy and opening new research avenues in texture characterization.

Contribution

It proposes a new fusion approach that leverages complex network topologies and neural networks for enhanced texture discrimination.

Findings

Outperforms existing texture analysis methods in accuracy

Demonstrates the effectiveness of combining complex networks with neural networks

Provides a new framework for deep texture feature extraction

Abstract

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.