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<p>Relaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation of contextual information, which is quantitatively expressed in terms of a set of "compatibility coefficients." The problem of determining compatibility coefficients has received a considerable attention in the past and many heuristic, statistical-based methods have been suggested. In this paper, the authors propose a rather different viewpoint to solve this problem: they derive them attempting to optimize the performance of the relaxation algorithm over a sample of training data; no statistical interpretation is given: compatibility coefficients are simply interpreted as real numbers, for which performance is optimal. Experimental results over a novel application of relaxation are given, which prove the effectiveness of the proposed approach.</p>
relaxation theory; learning (artificial intelligence); nonlinear programming; numerical analysis; probability; neural nets; iterative methods; pattern recognition; compatibility coefficients; relaxation labeling processes; image processing; pattern recognition; artificial intelligence; iterative procedures; local ambiguities; global consistency; contextual information; training data; real numbers

M. Refice and M. Pelillo, "Learning Compatibility Coefficients for Relaxation Labeling Processes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 933-945, 1994.
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