ELM based signature for texture classification

Texture classification is a very important field of image analysis. Because of this, the literature has been presented methods that provide more and more highly discriminative texture descriptors. This paper aims to contribute to the literature presenting a novel and powerful method to extract texture signature, based on weights of a single-hidden layer neural network called “Extreme Learning Machine” (ELM). We evaluate these descriptors in a classification experiment using three different texture datasets (and their rotated versions): Brodatz, Outex and Vistex. The results demonstrate a high classification accuracy of the method, proving that this novel methodology can be used successfully in computer vision problems.