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Surface texture classification is an important aspect of computer vision and a well studied problem. In this paper, we greatly increase speed for texture classification while maintaining accuracy. We take inspiration form past work and propose a new method for texture classification which is extremely fast due to the low dimensionality of our feature space. We extract distinctive features at a very early stage, thus removing the dependency on expensive and sensitive operations such as k-Means clustering which is used by much work in this field of research. We present experimental results on the Colombia-Utrecht Reflectance and Texture Database (CURET), to date the most challenging dataset for texture classification, and show that our method achieves comparable classification accuracy in comparison with the state-of-the-art, but at a 10-fold increased speed.