Unsupervised image segmentation using fuzzy c-means clustering based on region-level markov random field models

被引：0

作者：

Li, Pengwei ^{[1
]}

Ge, Wenying ^{[2
]}

机构：

[1] School of Software Engineering, Anyang Normal University, Anyang

[2] School of Computer and Information Engineering, Anyang Normal University, Anyang

来源：

Journal of Computational Information Systems | 2015年 / 11卷 / 13期

基金：

中国国家自然科学基金;

关键词：

Fuzzy C-means; Image Segmentation; Markov Random Field Model;

D O I：

10.12733/jcis14698

中图分类号：

学科分类号：

摘要：

In order to make use of the flexibility of fuzzy clustering methods and the sound special modeling capacities of region-level Markov random field models (RMRF), this paper attempts to combine RMRFbased image segmentation with the fuzzy c-means (FCM) algorithm and proposes an unsupervised segmentation algorithm, named FCM-RMRF. It uses the energy function of RMRF-based segmentation algorithm to define the dissimilarity function of FCM, which makes it possible to take into account both large-scale interactions between image regions and fuzzy nature of fuzzy clustering. Experiments on have shown that the proposed method can obtain more accurate segmentation results. ©, 2015, Journal of Computational Information Systems. All right reserved.

引用

页码：4895 / 4901

页数：6

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