Fusion of complex networks and randomized neural networks for texture analysis
This work addresses texture analysis for computer vision applications, presenting an incremental improvement by combining existing techniques.
The paper tackled texture analysis by fusing complex networks and randomized neural networks to create a discriminative signature, achieving results that surpassed many existing methods in accuracy.
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.