A Fuzzy Clustering Algorithm of Automatic Classification Based on EnFCM

Hong Wei Han; Lin Tian; Jia Qing Miao

doi:10.4028/www.scientific.net/AMR.989-994.1489

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994A Fuzzy Clustering Algorithm of Automatic...

A Fuzzy Clustering Algorithm of Automatic Classification Based on EnFCM

Abstract:

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

1489-1492

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.1489

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Online since:

July 2014

Authors:

Hong Wei Han*, Lin Tian, Jia Qing Miao

Keywords:

Enhanced Fuzzy C-Means Clustering, Gauss, Histogram, Image Segmentation, Moving Average Smoothing Filter, Peak Point Detection

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