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We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multi scale Gabor lter image representation. We discuss and compare a class of clustering objective functions which is systematically derived from invariance principles. As a general optimizationdoi:10.1109/34.709593 fatcat:ohmp67gezvfx5nfewhsrfhpctm