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A rotation and scale invariant texture classifier function is described for effective classification and segmentation of images involving textures of unknown rotation and scale changes. The classifier used is the texture energy associated with a mask that has been “tuned” to be both discriminant between different textures, and to be invariant to rotation and scale changes. The mask tuning scheme utilized is based on task-oriented criterion optimization via a guided random search procedure to incorporate the changes. Both a dynamic texture sample set using a two-dimensional (2D) linked list and a re-ranking procedure are applied for training. Maximum feature dispersion of inter texture classes and high feature convergence of inner texture class samples associated with other statistical measures are suggested as key criteria in training. In a study based on 15 distinct Brodatz textures it is found that: the tuning process …