Improving Fuzzy C-Means Clustering Based on Local Membership Variation

摘要：

The fuzzy c-means clustering algorithm has been successfully applied to a wide variety of problems. However, the image may be corrupted by noise, which leads to inaccuracy with segmentation. In the paper, a local fuzzy clustering regularization model is introduced in the objective function of the standard fuzzy c-means (FCM) algorithm. It can allow the membership of a pixel to be influenced by the memberships of its immediate neighborhood. Such schemes are useful for partition data sets affected by noise. Experimental results on both synthetic images and real image are given to demonstrate the effectiveness of the proposed algorithm.

关键词： fuzzy c-means image segmentation local fuzzy clustering regularization model

作者: Daiqiang Peng Yun Ling Yang Wang

作者单位: Nanjing Research Institute of Electronics Technology Nanjing, China

会议类型: 国际会议

会议名称: 2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)

会议地点: 厦门

会议语种:英文

页码: 346-350

在线出版日期: 2010-04-12（万方平台首次上网日期，不代表论文的发表时间）