Learning simple texture discrimination filters

Current texture analysis methods enable good discrimination but are computationally too expensive for applications which require high frame rates. This occurs because they use redundant calculations, failing in capturing the essence of the texture discrimination problem. In this paper we use a learning approach to obtain simple filters for this task. Although others have proposed learning-based methods, we are the first to simultaneously achieve discrimination rates comparable with state-of-the art methods at high frame rates. We particularize the general methodology to different filter structures, e.g., rotationally discriminant filters and rotationally invariant ones. We use Genetic Algorithms for learning and test our method against state-of-the-art ones, using the Brodatz album.