Image segmentation based on FCM with mahalanobis distance

Authors:
Yong Zhang;Zhuoran Li;Jingying Cai;Jianying Wang
Affiliations:
College of Computer & Information Technology, Liaoning Normal University, Dalian, China;College of Computer & Information Technology, Liaoning Normal University, Dalian, China;College of Computer & Information Technology, Liaoning Normal University, Dalian, China;College of Mathematics, Liaoning Normal University, Dalian, China
Venue:
ICICA'10 Proceedings of the First international conference on Information computing and applications
Year:
2010

Citing 5
Cited 0

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification

Neurocomputing
Novel modified fuzzy c-means algorithm with applications

Digital Signal Processing
Automatic image segmentation by dynamic region growth and multiresolution merging

IEEE Transactions on Image Processing
A contribution to convergence theory of fuzzy c-means and derivatives

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

For its simplicity and applicability, fuzzy c-means clustering algorithm is widely used in image segmentation. However, fuzzy c-means clustering algorithm has some problems in image segmentation, such as sensitivity to noise, local convergence, etc. In order to overcome the fuzzy c-means clustering shortcomings, this paper replaces Euclidean distance with Mahalanobis distance in the fuzzy c-means clustering algorithm. Experimental results show that the proposed algorithm has a significant improvement on the effect and efficiency of segmentation comparing with the standard FCM clustering algorithm.