Bayesian Image Classification with Baddeley's Delta Loss

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Abstract
In this article we adopt Baddeley's delta metric as a loss function in Bayesian image restoration and classification. We develop a new algorithm that allows us to approximate the corresponding optimal Bayesian estimator. With this algorithm good practical estimates can be obtained at approximately the same computational cost as traditional estimators like the marginal posterior mode (MPM). A comparison of our proposed classification with MPM shows significant advantages, especially with respect to fine structures.