13 November 2003 Unsupervised image segmentation using wavelet-domain hidden Markov models
Author Affiliations +
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
12 PAGES


SHARE
Back to Top