BAYESIAN IMAGE CLASSIFICATION WITH BADDELEY-DELTA-LOSS

this article we adopt Baddeley's delta metric as a loss function in Ba yesian image restoration and classification. We develop a new algorith m that allows us to approximate the corresponding optimal Bayesian est imator. With this algorithm good practical estimates can be obtained a t approximately the same computational cost as traditional estimators like the marginal posterior mode (MPM). A comparison of our proposed c lassification with MPM shows significant advantages, especially with r espect to fine structures.