Title
Noisy Image Segmentation by a Robust Clustering Algorithm Based on DC Programming and DCA
Abstract
We present a fast and robust algorithm for image segmentation problems via Fuzzy C-Means (FCM) clustering model. Our approach is based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) that have been successfully applied in a lot of various fields of Applied Sciences, including Machine Learning. In an elegant way, the FCM model is reformulated as a DC program for which a very simple DCA scheme is investigated. For accelerating the DCA, an alternative FCM-DCA procedure is developed. Moreover, in the case of noisy images, we propose a new model that incorporates spatial information into the membership function for clustering. Experimental results on noisy images have illustrated the effectiveness of the proposed algorithm and its superiority with respect to the standard FCM algorithm in both running-time and quality of solutions.
Year
DOI
Venue
2008
10.1007/978-3-540-70720-2_6
ICDM
Keywords
Field
DocType
dc program,fcm model,dc algorithms,robust clustering algorithm,clustering model,new model,dc programming,robust algorithm,proposed algorithm,noisy image,noisy image segmentation,standard fcm algorithm,simple dca scheme,membership function,machine learning,spatial information,convex function,image segmentation
Spatial analysis,Data mining,Computer science,Fuzzy logic,Image segmentation,Convex function,Artificial intelligence,Dc programming,Cluster analysis,Membership function,Machine learning,Applied science
Conference
Volume
ISSN
Citations 
5077
0302-9743
3
PageRank 
References 
Authors
0.42
16
4
Name
Order
Citations
PageRank
Le Thi Hoai An1103880.20
Le Hoai Minh21317.91
Nguyen Trong Phuc3121.69
Pham Dinh Tao41340104.84