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

Abstract

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

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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Neutrosophic fuzzy clustering segmentation algorithm based on HMRF

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