# Unsupervised Bayesian wavelet domain segmentation using Potts-Markov random field modeling

@article{Brault2005UnsupervisedBW, title={Unsupervised Bayesian wavelet domain segmentation using Potts-Markov random field modeling}, author={Patrice Brault and Ali Mohammad-Djafari}, journal={J. Electronic Imaging}, year={2005}, volume={14}, pages={043011} }

We describe a new fully unsupervised image segmentation method based on a Bayesian approach and a Potts-Markov random field (PMRF) model that are performed in the wavelet domain. A Bayesian segmentation model, based on a PMRF in the direct domain, has already been successfully developed and tested. This model performs a fully unsupervised segmentation, on images composed of homogeneous regions, by introducing a hidden Markov model (HMM) for the regions to be classified, and Gaussian…

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## References

SHOWING 1-10 OF 36 REFERENCES

### Bayesian Wavelet Domain Segmentation

- Computer Science
- 2004

A new version of the fully unsupervised segmentations of still images and 2D+T sequences is demonstrated by Bayesian methods, on the basis of a Hidden Markovian Model and a Potts‐Markov Random Field, in the pixel domain by taking advantage of the local decay property, or “peaky” distribution of the wavelet coefficients, in an orthogonal decomposition.

### A multiscale random field model for Bayesian image segmentation

- Computer Science, MathematicsIEEE Trans. Image Process.
- 1994

Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing, and is found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.

### Bayesian segmentation and motion estimation in video sequences using a Markov-Potts model

- Mathematics, Computer Science
- 2004

A new approach for the segmentation of video sequences based on recent works on the Bayesian segmentation and fusion is reported here, making the assumption that the images follow a Potts-Markov random field model.

### Unsupervised Bayesian image segmentation using wavelet-domain hidden Markov models

- Computer ScienceProceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)
- 2003

A new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs that can achieve high classification accuracy that is close to the supervised one.

### Unsupervised image segmentation using wavelet-domain hidden Markov models

- Computer ScienceSPIE Optics + Photonics
- 2003

A new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs, where three clustering methods are used to obtain the initial segmentation results.

### Multiscale Bayesian segmentation using a trainable context model

- Computer ScienceIEEE Trans. Image Process.
- 2001

A multiscale Bayesian segmentation algorithm which can effectively model complex aspects of both local and global contextual behavior is proposed which makes the method flexible by allowing both the context and the image models to be adapted without modification of the basic algorithm.

### Multiscale image segmentation using wavelet-domain hidden Markov models

- Computer ScienceIEEE Trans. Image Process.
- 2001

A new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree model, which can directly segment wavelet-compressed images without the need for decompression into the space domain.

### Bayesian tree-structured image modeling using wavelet-domain hidden Markov models

- Computer ScienceOptics & Photonics
- 1999

This paper proposes a fast shift-invariant HMT estimation algorithm that outperforms all other wavelet- based estimators in the current literature, both in mean- square error and visual metrics.

### Image fusion and unsupervised joint segmentation using a HMM and MCMC algorithms

- Computer Science, MathematicsJ. Electronic Imaging
- 2005

A Bayesian framework for unsupervised image fusion and joint segmentation based on a hidden Markov modeling of the images where the hidden variables represent the common classification or segmentation labels is proposed.

### Mean field annealing using compound Gauss-Markov random fields for edge detection and image estimation

- Computer ScienceIEEE Trans. Neural Networks
- 1993

A deterministic relaxation method based on mean field annealing with a compound Gauss-Markov random (CGMRF) field model is proposed and a set of iterative equations for the mean values of the intensity and both horizontal and vertical line processes with or without taking into account some interaction between them are presented.