2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:MA-P2.10
Session:Active-Contour, Level-Set, and Cluster-Based Segmentation Methods
Time:Monday, September 17, 09:50 - 12:30
Presentation: Poster
Title: A HIERARCHICAL CLUSTERING BASED ON MUTUAL INFORMATION MAXIMIZATION
Authors: Mehdi Aghagolzadeh; Control and Intelligent Processing Center of Excellence 
 Hamid Soltanian-Zadeh; Control and Intelligent Processing Center of Excellence 
 Babak Nadjar Araabi; Control and Intelligent Processing Center of Excellence 
 Ali Aghagolzadeh; University of Tabriz 
Abstract: Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.



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