Improved hidden Markov models in the wavelet-domain

Authors:
Guoliang Fan;Xiang-Gen Xia
Affiliations:
Dept. of Electr. & Comput. Eng., Delaware Univ., Newark, DE;-
Venue:
IEEE Transactions on Signal Processing
Year:
2001

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Abstract

Wavelet-domain hidden Markov models (HMMs), in particular the hidden Markov tree (HMT) model, have been introduced and applied to signal and image processing, e.g., signal denoising. We develop a simple initialization scheme for the efficient HMT model training and then propose a new four-state HMT model called HMT-2. We find that the new initialization scheme fits the HMT-2 model well. Experimental results show that the performance of signal denoising using the HMT-2 model is often improved over the two-state HMT model developed by Crouse et al. (see ibid., vol.46, p.886-902, 1998)