Markov random field based on Kullback-Leibler divergence and its applications to geo-spatial image segmentation

被引：0
作者：
Nishii, R [1 ]
机构：
[1] Hiroshima Univ, Fac Integrated Arts & Sci, Higashihiroshima 7398521, Japan
来源：
6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XVII, PROCEEDINGS: INDUSTRIAL SYSTEMS AND ENGINEERING III | 2002年
关键词：
divergence; ICM; Mahalanobis distance; MAP; MRF; multispectral data;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider segmentation of geo-spatial images by Markov random fields. MRFs whose distribution is specified by the Kullback-Leibler divergence between class-conditional densities are introduced for the spatial model. It is shown that the model is a natural extension of Switzer's assumption, and the estimation method of the parameters is established by maximizing the pseudo likelihood. The method is applied to benchmark data, and it shows a good performance.
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页码：399 / 405
页数：7
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