Fusion of complex networks and randomized neural networks for texture analysis

• A high texture descriptor is proposed. • It is based on the fusion of complex networks and randomized neural networks. • Texture images are modelled as complex networks with different scales. • Network topological characteristics are used to train a randomized neural network. • The output weights of the randomized neural network are used as a texture descriptors. 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 network and its topological properties as well as the image pixels are used to train randomized neural networks to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracy 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.