Weight selection in W-K-means algorithm with an application in color image segmentation

Authors:
Wen-Liang Hung;Yen-Chang Chang;E. Stanley Lee
Affiliations:
Department of Applied Mathematics, National Hsinchu University of Education, Hsinchu 30013, Taiwan;Department of Applied Mathematics, National Hsinchu University of Education, Hsinchu 30013, Taiwan;Department of Industrial and Manufacturing Systems Engineering, Kansas State University, KS 66506, USA
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

In this paper, a weight selection procedure in the W-k-means algorithm is proposed based on the statistical variation viewpoint. This approach can solve the W-k-means algorithm's problem that the clustering quality is greatly affected by the initial value of weight. After the statistics of data, the weights of data are designed to provide more information for the character of W-k-means algorithm so as to improve the precision. Furthermore, the corresponding computational complexity is analyzed as well. We compare the clustering results of the W-k-means algorithm with the different initialization methods. Results from color image segmentation illustrate that the proposed procedure produces better segmentation than the random initialization according to Liu and Yang's (1994) evaluation function.