Unsupervised image segmentation using wavelet-domain hidden Markov models

Xiaomu Song; Guoliang Fan

doi:10.1117/12.507049

13 November 2003 Unsupervised image segmentation using wavelet-domain hidden Markov models

Xiaomu Song, Guoliang Fan

Author Affiliations +

Proceedings Volume 5207, Wavelets: Applications in Signal and Image Processing X; (2003) https://doi.org/10.1117/12.507049
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs), where three clustering methods are used to obtain the initial segmentation results. We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. Three clustering methods, i.e., K-mean, soft clustering and multiscale clustering, are studied to convert the unsupervised segmentation problem into the self-supervised process by identifying the reliable training samples. The simulation results on synthetic mosaics and real images show that the proposed unsupervised segmentation algorithms can achieve high classification accuracy.

Citation Download Citation

Xiaomu Song, Xiaomu Song, Guoliang Fan, Guoliang Fan, "Unsupervised image segmentation using wavelet-domain hidden Markov models", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.507049; https://doi.org/10.1117/12.507049

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