嵌入隐马尔科夫随机场的中智模糊聚类算法

发布时间：2018-05-14 05:07

  本文选题：图像分割 + 模糊聚类　； 参考：《西安电子科技大学学报》2017年06期

【摘要】：针对中智模糊C均值聚类算法抗噪能力弱的问题,提出嵌入隐马尔科夫随机场的中智模糊聚类分割算法.利用隐马尔科夫随机场描述图像任意像素分类的先验信息,将其与样本分类隶属度之间的信息散度作为正则项,嵌入现有中智模糊聚类目标函数;同时,将欧氏空间样本通过核函数映射至高维空间,采用最优化方法获得隐马尔科夫随机场的核空间中智模糊聚类分割的迭代表达式.对标准的、现场采集的以及人工合成的3类灰度图像添加一定强度的高斯和椒盐噪声进行分割测试,实验结果表明,这种分割算法相比基于隐马尔科夫随机场的模糊C均值聚类等分割算法的抗噪性能,有了显著提高.
[Abstract]:In order to solve the problem of weak anti-noise ability of the middle intelligence fuzzy C-means clustering algorithm, a new algorithm of middle intelligence fuzzy clustering segmentation based on embedding hidden Markov random field is proposed. Using hidden Markov random field to describe the priori information of any pixel classification of image, the information divergence between it and the membership degree of sample classification is taken as the regular item, and the existing objective function of fuzzy clustering is embedded. The sample of Euclidean space is mapped to high dimensional space by kernel function, and the iterative expression of intelligent fuzzy clustering segmentation in kernel space of hidden Markov random field is obtained by optimization method. The standard, on-site and synthetic grayscale images with certain intensity of Gao Si and salt noise are segmented. The experimental results show that, Compared with the fuzzy C-means clustering algorithm based on Hidden Markov Random Field, the proposed segmentation algorithm has a better performance against noise.
【作者单位】： 西安邮电大学电子工程学院;
【基金】：国家自然科学基金重点资助项目(61136002) 陕西省自然科学基金资助项目(2014JM8331,2014JQ5183,2014JM8307) 陕西省教育厅科学研究计划资助项目(2015JK1654)
【分类号】：TP391.41
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本文编号：1886461