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Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach

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2 Author(s)

We approach the problem of labeling a set of objects from a quantitative standpoint. We define a world model in terms of transition probabilities and propose a definition of a class of global criteria that combine both ambiguity and consistency. A projected gradient algorithm is developed to minimize the criterion. We show that the minimization procedure can be implemented in a highly parallel manner. Results are shown on several examples and comparisons are made with relaxation labeling techniques.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-3 ,  Issue: 4 )