A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-Based Image Segmentation

J. Zhang, J. W. Modestino

Research output: Contribution to journalArticle

78 Scopus citations

Abstract

A clustering scheme is used for model parameter estimation. Most of the existing clustering procedures require prior knowledge of the number of classes, which is often, as in unsupervised image segmentation, unavailable and must be estimated. This problem is known as the cluster validation problem. For unsupervised image segmentation the solution of this problem directly affects the quality of the segmentation. A model-fitting approach to the cluster validation problem based on Akaike's information criterion (AIC) is proposed, and its efficacy and robustness are demonstrated through experimental results for synthetic mixture data and image data.

Original languageEnglish (US)
Pages (from-to)1009-1017
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume12
Issue number10
DOIs
StatePublished - Oct 1990

Keywords

  • Cluster validation
  • computer vision
  • image interpretation
  • scene segmentation
  • texture discrimination

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-Based Image Segmentation'. Together they form a unique fingerprint.

  • Cite this