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Segmentation of textured images using a multiresolution Gaussian autoregressive model.

M L Comer¹, E J Delp

¹Thomson Consumer Electronics, Indianapolis, IN 47907, USA.

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

|February 12, 2008

Summary

This study introduces a novel multiresolution Bayesian algorithm for segmenting textured images. The new method, multiresolution maximization of the posterior marginals (MMPM), improves upon previous techniques for accurate image segmentation.

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Area of Science:

Computer Vision
Image Processing
Statistical Modeling

Background:

Accurate segmentation of textured images is crucial for various applications.
Existing multiresolution segmentation methods often rely on less optimal Maximum a Posteriori (MAP) estimation.
There is a need for improved Bayesian approaches that capture inter-level correlations in image and label pyramids.

Purpose of the Study:

To develop a new algorithm for textured image segmentation using a multiresolution Bayesian framework.
To introduce the multiresolution maximization of the posterior marginals (MMPM) estimation criterion.
To address limitations of previous Maximum a Posteriori (MAP) based multiresolution segmentation.

Main Methods:

Utilizes a multiresolution Gaussian autoregressive (MGAR) model for image representation.

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Employs a multiscale Markov random field model for class label pyramids.

Incorporates correlations between different levels of image and label pyramids.

Applies a modified expectation-maximization (EM) algorithm for parameter estimation.

Main Results:

The proposed MMPM estimate is a natural extension of single-resolution MPM, offering improved segmentation.
The algorithm effectively handles unknown texture parameters using the EM algorithm.
Experimental results demonstrate the algorithm's performance in textured image segmentation.

Conclusions:

The developed multiresolution Bayesian algorithm provides an effective approach for textured image segmentation.
The MMPM criterion is shown to be more appropriate than MAP for this task.
The integration of MGAR and multiscale Markov random field models enhances segmentation accuracy.