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Texture is a visual attribute largely used in many problems of imageanalysis. Currently, many methods that use learning techniques have beenproposed for texture discrimination, achieving improved performance overprevious handcrafted methods. In this paper, we present a new approach thatcombines a learning technique and the Complex Network (CN) theory for textureanalysis. This method takes advantage of the representation capacity of CN tomodel a texture image as a directed network and uses the topologicalinformation of vertices to train a randomized neural network. This neuralnetwork has a single hidden layer and uses a fast learning algorithm, which isable to learn local CN patterns for texture characterization. Thus, we use theweighs of the trained neural network to compose a feature vector. These featurevectors are evaluated in a classification experiment in four widely used imagedatabases. Experimental results show a high classification performance of theproposed method when compared to other methods, indicating that our approachcan be used in many image analysis problems.

Learning Local Complex Features using Randomized Neural Networks for Texture Analysis