Paper Info
Title
Unsupervised Image Segmentation Using Automated Fuzzy c-Means
Abstract
An unsupervised fuzzy clustering technique, Fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the conventional FCM algorithm must be estimated by expertise users to determine the cluster numbers. To overcome the limitation of FCM algorithm, an automated fuzzy c-mean (AFCM) algorithm is presented in this paper. The proposed algorithm initiates the first two centroids of clusters by a method based on Otsu algorithm and automatically determines the appropriate cluster number for image segmentation. The performance of the proposed technique has been tested with reference to conventional FCM. The experimental results demonstrate that AFCM can spontaneously estimate the appropriate number of clusters and its performance is faster convergence than the performance of the conventional FCM. Keywords : Fuzzy c-Means, Image segmentation, Clustering, Otsu algorithm.
Year
DOI
Venue
2007
10.1109/CIT.2007.181
CIT
Keywords
Field
DocType
unsupervised fuzzy clustering technique,unsupervised image segmentation,automated fuzzy c-mean,conventional fcm,conventional fcm algorithm,clustering algorithm,fcm algorithm,fuzzy c-means,proposed algorithm,automated fuzzy c-means,otsu algorithm,image segmentation,fuzzy clustering,unsupervised learning,fuzzy set theory
k-means clustering,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Scale-space segmentation,Pattern recognition,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Cluster analysis
Conference
ISBN
Citations 
PageRank 
0-7695-2983-6
5
0.59
References 
Authors
2
2
Name
Order
Citations
PageRank
Supatra Sahaphong150.59
Nualsawat Hiransakolwong2214.74