Abstract: Convolution of textured images with a set of small masks can be used to produce features for texture classification or for image segmentation. These masks are usually picked from a standard set, such as Laws' texture energy operators or the variations discussed by other researchers. In this paper we discuss using simulated annealing to determine an optimum set of masks for particular sets of textures. Initial masks are picked and iteratively improved by using simulated annealing with an appropriate energy function. The masks converge to a final set that minimizes the energy function. We show experimental results with a small set of textures. The masks produced by the method depends on the textures used, and these masks are more effective in discriminating between these textures than the standard masks.!10
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