计算机与现代化 ›› 2012, Vol. 1 ›› Issue (11): 78-80+1.doi: 10.3969/j.issn.1006-2475.2012.11.021

• 图像处理 • 上一篇    下一篇

变权重MRF算法在图像自动无监督分割中的应用

刘雪娜,侯宝明   

  1. 渤海大学信息科学与技术学院,辽宁 锦州 121013
  • 收稿日期:2012-06-21 修回日期:1900-01-01 出版日期:2012-11-10 发布日期:2012-11-10

Weighted MRF Algorithm for Automatic Unsupervised Image Segmentation

LIU Xue-na, HOU Bao-ming   

  1. College of Information Science and Technology, Bohai University, Jinzhou 121013, China
  • Received:2012-06-21 Revised:1900-01-01 Online:2012-11-10 Published:2012-11-10

摘要: 为了实现图像的自动无监督分割,本文提出类自适应变权重马尔可夫随机场分割算法。首先结合最小描述长度准则,自适应计算马尔可夫随机场框架下的图像分类数;然后引入变权重的马尔可夫随机场算法,扩大势函数的选择范围,消除势函数的复杂计算;最后用迭代条件模式进行优化,获得最大后验概率准则下的分割图像。在Matlab环境中的测试结果表明,该算法具有实效性,能正确计算分类数,同时有效减少了分割错误。

关键词: 变权重马尔可夫随机场, 最小描述长度准则, 图像分割, 无监督分割, 迭代条件模式, MAP准则

Abstract: In order to achieve the automatic unsupervised image segmentation, an algorithm based on the adaptive classification and the weighted MRF is proposed. First, combined with the MDL criterion, the number of image classification under the framework of Markov random fields is computed adaptively. And then, the weighted MRF algorithm is used to expand the option range of the potential function, thus to eliminate the complex calculation of the potential function. Finally, by using ICM algorithm to optimize the model, the segmentation image under MAP criterion is obtained. In the Matlab, test results show that the proposed algorithm is effective, which can correctly calculate the number of classification and effectively reduce the segmentation error.

Key words: weighted MRF, MDL, image segmentation, unsupervised segmentation, ICM, MAP criterion

中图分类号: