Adaptive clustering algorithm for image segmentation

Thrasyvoulos N. Pappas*, N. S. Jayant

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


A generalization of the K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image is proposed. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in an iterative procedure involving averaging over a sliding window whose size decreases as the algorithm progresses. Results with an eight-neighbor Gibbs random field model applied to pictures of industrial objects and a variety of other images show that the algorithm performs better than the K-means algorithm and its nonadaptive extensions.

Original languageEnglish (US)
Pages (from-to)1667-1670
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Dec 1 1989
Event1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
Duration: May 23 1989May 26 1989

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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