嵌入隐马尔科夫随机场的中智模糊聚类算法

doi:10.3969/j.issn.1001-2400.2017.06.019

西安电子科技大学学报

嵌入隐马尔科夫随机场的中智模糊聚类算法

吴成茂;上官若愚

(西安邮电大学电子工程学院，陕西西安 710121)

收稿日期:2016-12-30 出版日期:2017-12-20 发布日期:2018-01-18
通讯作者: 上官若愚(1992-)，女，西安邮电大学硕士研究生, E-mail: sg604761731@126.com
作者简介:吴成茂(1968-)，男，高级工程师, E-mail:wuchengmao123@sohu.com

Neutrosophic fuzzy clustering segmentation algorithm based on HMRF

WU Chengmao;SHANGGUAN Ruoyu

(School of Electronic Engineering , Xi'an Univ. of Posts and Telecommunications, Xi'an 710121,China)

Received:2016-12-30 Online:2017-12-20 Published:2018-01-18
Supported by:
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)

摘要/Abstract

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．

关键词: 图像分割, 模糊聚类, 中智聚类, 隐马尔科夫随机场, 核函数

Abstract:

Considering neutrosophic C-means clustering algorithm with weak ability of suppressing noise, a neutrosophic C-means clustering segmentation algorithm based on the hidden Markov random field is proposed. First, the hidden Markov random field is used to describe the prior information of the arbitrary pixels classification. Second, information divergence between the prior information and sample classification membership is taken as a regular term and embedded into the existing neutrosophic C-means clustering objective function. Third, the samples in the European Space is mapped into the high-dimensional space through the kernel function, and the iterative expression for the neutrosophic C-means clustering segmentation algorithm based on the hidden Markov random field is obtained by the optimization method. Many standard, actual, and synthetic images corrupted by noise are used to validate the segmentation performance of the improved clustering segmentation algorithm. Experimental results show that the anti-noise performance of the proposed segmentation algorithm is improved significantly than the fuzzy C-means clustering algorithm based on the hidden Markov random field, and other fuzzy clustering segmentation algorithms.

Key words: image segmentation, fuzzy clustering, neutrosophic C-means clustering, hidden Markov random field, kernel function

吴成茂;上官若愚. 嵌入隐马尔科夫随机场的中智模糊聚类算法[J]. 西安电子科技大学学报, 2017, 44(6): 103-108+121.

WU Chengmao;SHANGGUAN Ruoyu. Neutrosophic fuzzy clustering segmentation algorithm based on HMRF[J]. Journal of Xidian University, 2017, 44(6): 103-108+121.

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嵌入隐马尔科夫随机场的中智模糊聚类算法

Neutrosophic fuzzy clustering segmentation algorithm based on HMRF

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