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。
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.
出处
《计算机与现代化》
2012年第11期78-80,166,共3页
Computer and Modernization
基金
辽宁省教育厅科学技术研究项目(L2010006)
