Soft Computing

, Volume 11, Issue 2, pp 103–113

Possibility Theoretic Clustering and its Preliminary Application to Large Image Segmentation

  • Fu-lai Chung
  • Shitong Wang
  • M. Xu
  • Dewen Hu
  • Qing Lin
Original Paper

DOI: 10.1007/s00500-006-0056-8

Cite this article as:
Chung, F., Wang, S., Xu, M. et al. Soft Comput (2007) 11: 103. doi:10.1007/s00500-006-0056-8

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.

Keywords

Exponential possibility distributionConsistent functionsClustering algorithmEpanechnikov kernel functionsBiased samplingLarge image segmentation

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Fu-lai Chung
    • 2
  • Shitong Wang
    • 1
  • M. Xu
    • 1
  • Dewen Hu
    • 3
  • Qing Lin
    • 4
  1. 1.School of Information EngineeringSouthern Yangtze UniversityWuxiChina
  2. 2.Department of ComputingHong Kong Polytechnic UniversityHong KongChina
  3. 3.School of AutomationNational Defense University of Science and TechnologyChangshaChina
  4. 4.Department of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina