An Automatic Fuzzy Image Segmentation Method Based on Wavelet Energy Histograms and Markov Random Field Models

被引：0

作者：

Liu, Guo-ying ^{[1
]}

Song, Xu ^{[1
]}

Hong, Dan ^{[1
]}

机构：

[1] Anyang Normal Univ, Dept Comp & Informat Engn, Anyang 455002, Peoples R China

来源：

2ND INTERNATIONAL CONFERENCE ON APPLIED MECHANICS, ELECTRONICS AND MECHATRONICS ENGINEERING (AMEME) | 2017年

基金：

中国国家自然科学基金;

关键词：

Image segmentation; Fuzzy clustering; Markov random field models; Wavelet energy histogram; LOCAL INFORMATION;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Fuzzy C-Means (FCM) algorithm has been widely used in remote sensed image segmentation. However, it has two main defects: (1) its sensitiveness to noise outliers, and other imaging artifacts; (2) the number of clusters needed to be set previously. In order to overcome these problems, in this paper, we incorporate the wavelet energy histograms (WEHs) and Markov random field models (MRFs) into the fuzzy clustering procedure and present a novel image segmentation method. WEHs serve the determination of cluster centers and MRFs play a role of modelling spatial information. First of all, the peaks of wavelet histogram are exploited to find the initial cluster centers. Then, a fuzzy clustering procedure with MRFs is performed on each band separately. Finally, the fused label of the clustering result from each band is used as the final segmentation result. The superiority of the proposed method is demonstrated by comparing it with the some well-known methods of FCM, FLICM, HMRF-FCM, and AHFCM.

引用

页码：205 / 212

页数：8

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