Bayesian image classification with Baddeley's delta loss

被引：6

作者：

Frigessi, A ^{[1
]}

Rue, H ^{[1
]}

机构：

[1] NORWEGIAN UNIV SCI & TECHNOL,DEPT MATH SCI,N-7034 TRONDHEIM,NORWAY

来源：

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | 1997年 / 6卷 / 01期

关键词：

asymmetric loss functions; Bayesian inference; distance between binary images; image restoration; Markov chain Monte Carlo methods; metropolis algorithm;

D O I：

10.2307/1390724

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：55 / 73

页数：19

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