Image Segmentation Using Credibility Critical Values
In this paper, a method of creating fuzzy data matrix (a matrix whose elements are fuzzy variables) representing an image is proposed. Further, a study on image segmentation carried out on the basis of such a matrix is carried out. This work considers three methods of crisp conversion available in credibility theory. Their performances have been evaluated under k-means clustering and rough k-means clustering with the help of a mammogram using appropriate validity measures.
Cite this Article
S. Sampath, R. Senthil Kumar. Image Segmentation Using Credibility Critical Values. Journal of Image Processing & Pattern Recognition Progress. 2015; 2(2): 46–55p.
Uso AM, Pla F, Sevila PG. Unsupervised Image Segmentation using a Heirarchical Clustering Selection Process. Structural Syntactic and Statistical Pattern Recognition.2006; 4109: 799–807p.
Arifin AZ, Asano A. Image Segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recognition Letters. 2006; 27(13): 1515–1521p.
Chinki Chandhok, Soni Chaturvedi, Khurshid AA. An approach to Image segmentation using k-means clustering algorithm. International Journal of Information Technology (IJIT). 2012; 1(1): 11–17p.
Marroquin JL, Girosi F. Some Extentions of the K-Means Algorithm For Image Segmentation and Pattern Classification. Technical Report,MIT Artificial Intelligence Laborartory.1993.
Luo M, Ma YF, Zhang HJ. A Special Constrained K-Means approach to Image Segmentation. Proc.The 2003 Joint Conference of Fourth International Conference on Informations Communications and Signal Processing and the Fourth Pacific Rim Conference on Multimedia. 2003; 2: 738–742p.
Dubey Shiv Ram, Dixit Pushkar, Singh Nishant et al. Infected Fruit part Detection using K-means clustering segmentation technique. International Journal of Artificial Intelligence and Interactive Multimedia. 2013; 2(2): 65–72p.
Macqueen J. Some methods for classification and analysis of multivariate observations. In Proc. of the 5th Berkeley Symp. On Mathematical Statistics and Proabability, University of California Press, 1967; 281–297p.
Lingras P, West C. Interval set clustering of web users with rough k–means. J.Intell. Inf.Syst, 2004; 23(1): 5–16p.
Peters G. Some refinements of rough k–means clustering. Pattern Recognition. 2006; 39: 1481–1491p.
Davies DL, Bouldin DW. A cluster seperation measure. IEEE. Trans. Pattern Anal. Mach. Intell. 1979; 1: 224–227p.
Liu B. Uncertainty Theory.http://orsc.edu.cn/liu/ut.pdf, 3rd ed., 2008.
Sampath S, Kalaivani R. Clustering of Fuzzy data using credibilistic critical values. In: IEEE International Conference on Signal and Image Processing. 2010; 227–232p.
Sampath S, Senthil Kumar R. Clustering of Fuzzy data using Credibilistic Expected and Critical Values. In: IEEE International Conference on Computer Communication and Systems.2014: 176–181p.
Sampath S, Senthil Kumar R. Fuzzy Clustering using Credibilistic Critical Values. International Journal of Computational Intelligence and Informatics. 2013; 3(3): 213–221p.
Sampath S, Senthil Kumar R. Rough k-means clustering using Credibilistics Critical Values. International Journal of Data Mining and Emerging Technologies (IJDMET).
- There are currently no refbacks.