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Image segmentation using hidden Markov Gauss mixture models.

Kyungsuk Pyun¹, Johan Lim, Chee Sun Won

¹Imaging and Printing Group, Hewlett-Packard Company, San Diego, CA 92127, USA. kspyun@gmail.com

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

|July 4, 2007

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This study introduces a novel multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs). HMGMMs offer improved classification accuracy and spatial homogeneity compared to existing techniques for image processing tasks.

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Image segmentation is crucial for simplifying complex image processing tasks.
Existing methods have limitations in classification accuracy and spatial homogeneity.

Purpose of the Study:

To develop a new multiclass image segmentation method.
To evaluate the performance of the proposed method against established techniques.

Main Methods:

Utilized hidden Markov Gauss mixture models (HMGMMs) for multiclass segmentation.
Employed supervised learning with Gauss mixture estimation via vector quantization and minimum discrimination information distortion.
Formulated segmentation using maximum a posteriori criteria and a stochastic expectation-maximization algorithm.

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Main Results:

HMGMM demonstrated superior classification performance, reducing Bayes risk.
Achieved greater spatial homogeneity in segmented objects compared to classification and regression trees, learning vector quantization, causal HMMs, and multiresolution HMMs.
Computational load was comparable to causal HMMs.

Conclusions:

HMGMM is an effective method for multiclass image segmentation.
The proposed approach offers significant advantages in accuracy and spatial consistency.
Suitable for aerial image and texture segmentation applications.