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 language | English (US) |
|---|---|
| Pages (from-to) | 1009-1017 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 12 |
| Issue number | 10 |
| DOIs | |
| State | Published - 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
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Cite this
Zhang, J., & Modestino, J. W. (1990). A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-Based Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), 1009-1017. https://doi.org/10.1109/34.58873