Color-texture classification based on spatio-spectral complex network representations

This paper proposes a method for color-texture analysis by learning spatio-spectral representations from a complex network framework using the Randomized Neural Network (RNN). We model the color-texture image as a directed complex network based on the Spatio-Spectral Network (SSN) model, which considers within-channel connections in its topology to represent the spatial characteristics and spectral patterns covered by between-channel links. The insight behind the method is that complex topological features from the SSN can be embedded by a simple and fast neural network model for color-texture classification. Thus, we investigate how to effectively use the RNN to analyze and represent the spatial and spectral patterns from the SSN. We use the SSN vertex measurements to train the RNN to predict the dynamics of the complex network evolution and adopt the learned weights of the output layer as descriptors. Classification experiments in four datasets show the proposed method produces a very discriminative representation. The results demonstrate that our method obtains accuracies higher than several literature techniques, including deep convolutional neural networks. The proposed method also showed to be promising for plant species recognition, achieving high accuracies in this task. This performance indicates that the proposed approach can be employed successfully in computer vision applications.