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The ill-defined nature of the segmentation problem makes the selection of the optimal image partition difficult. One can characterize image segmentation as an attempt to find the best possible representation of a data set using a certain number of ldquoobjects.rdquo This can be regarded as data information compression, resulting in the distortion of the original values. Data sets are well represented when the correct number of regions is chosen. The concept behind this approach is similar to the main problem of rate distortion theory: A finite set of code words is chosen to approximate the numbers or source symbols as well as possible. In our approach, the number of regions is equivalent to the number of code words. The mean of a region provides canonical representation of respective group members, and the distortion function is the mean-square error assuring a good evaluation method for image segmentation.