Possibility Theoretic Clustering and its Preliminary Application to Large Image Segmentation

Abstract

Rooted at the exponential possibility model recently developed by Tanaka and his colleagues, a new clustering criterion or concept is introduced and a possibility theoretic clustering algorithm is proposed. The new algorithm is characterized by a novel formulation and is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzen window technique and Yager's probability---possibility transformation. Our experimental results demonstrate its success in artificial datasets and large image segmentation. In order to reduce the complexity of large image segmentation, we propose to integrate the new clustering algorithm with a biased sampling procedure based on Epanechnikov kernel functions. As demonstrated by the preliminary experimental results, the possibility theoretic clustering is effective in image segmentation and its integration with a biased sampling procedure offers an attractive framework of large image processing.