Paper Info
Title
An automatic fuzzy c-means algorithm for image segmentation
Abstract
Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.
Year
DOI
Venue
2010
10.1007/s00500-009-0442-0
Soft Comput.
Keywords
Field
DocType
fuzzy clustering,cluster number,better segmentation quality,fuzzy c-means,image segmentationfuzzy c-means � fuzzy clusteringk-meansspatial information,spatial information,image segmentation,k-means,automatic fuzzy clustering algorithm,membership function,standard fcm algorithm,appropriate number,amfcm algorithm,segmentation algorithm,automatic fuzzy c-means algorithm,afcm algorithm,k means
Fuzzy clustering,Data mining,Scale-space segmentation,Fuzzy classification,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,FLAME clustering,Canopy clustering algorithm,k-means clustering,Pattern recognition,Algorithm,Machine learning
Journal
Volume
Issue
ISSN
14
2
1433-7479
Citations 
PageRank 
References 
16
0.78
10
Authors
2
Name
Order
Citations
PageRank
Yan-ling Li1214.28
Y. Shen2626.05