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.

引用

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页码：399 / 405

页数：7

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