A COMPREHENSIVE SURVEY OF CONTEMPORARYRESEARCHES ON IMAGE SEGMENTATION THROUGHCLUSTERING
Keywords:
Clustering, Image segmentation, Exclusive Clustering, Overlapping Clustering, Hierarchical clustering, Probabilistic D-ClusteringAbstract
This paper presents an analysis on different clustering techniques for image segmentation. Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). Clustering is most widely spread approach in Image segmentation because of its robust characteristics for data classification. Clustering is done on different attributes of an image such as size, color, texture etc. The purpose of clustering is to get meaningful result, effective storage and fast retrieval in various areas.
References
www.wikipedia.com
Andrew Moore: “K-means and Hierarchical Clustering - Tutorial Slides”
Brian T. Luke: “K-Means Clustering”
J. C. Dunn (1973): "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 32-5
J. C. Bezdek (1981): "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New York.
Data Clustering A Review ACM Computing Surveys, Vol. 31, No. 3, September 1999
Paper 193-2011 Comparison of Probabilistic-D and k-Means Clustering in Segment Profiles for B2B Markets SAS Global Forum 2011
A Survey on Image Segmentation Through Clustering International Journal of Research and Reviews in Information Sciences Vol. 1, No. 1, March 2011
Image Segmentation using Fuzzy Clustering: A Survey. 6 th International Conference on Emerging Technologies (ICET) 2010
Downloads
Published
Issue
Section
License
Copyright (c) 2011 International Journal of Computer Science and Engineering Research and Development (IJCSERD)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
