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A Markov random field model-based approach to unsupervised texture segmentation using local and global spatial

C Kervrann¹, F Heitz

¹IRISA/INRIA, Rennes.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1995

Summary

This study introduces an unsupervised texture segmentation method using spatial statistics and Markov random fields. It effectively segments images without prior knowledge of texture parameters or region count.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Statistical model-based texture segmentation requires prior knowledge of model parameters and region count.
Existing methods often lack flexibility in handling unknown texture characteristics.

Purpose of the Study:

To present an unsupervised texture segmentation method.
To eliminate the need for prior knowledge of texture regions, parameters, or class numbers.

Main Methods:

Analysis of local and global second and higher-order spatial statistics.
Modeling the segmentation map using an augmented-state Markov random field.
Incorporation of an outlier class for dynamic region creation.
Bayesian estimation of the segmentation map via deterministic relaxation.

Main Results:

Successful segmentation of real-world textured images without prior parameter or region knowledge.
Demonstration of the algorithm's ability to dynamically create new regions.

Conclusions:

The proposed unsupervised method offers a robust alternative to model-based segmentation.
This approach enhances flexibility in texture analysis for complex, real-world images.