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

J. Zhang; J. W. Modestino

doi:10.1109/34.58873

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

J. Zhang, J. W. Modestino

Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

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 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	https://doi.org/10.1109/34.58873
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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