Image Segmentation Based on Wavelet Domain Hierarchical Markov Model
基于小波域层次Markov模型的图像分割

In order to overcome the deficiency of approximation to the wavelet coefficient joint probability with two-state Gaussian mixture model(GMM) and the shortcoming of the independence between wavelet labels in wavelet domain hidden Markov tree model(HMT),a new image segmentation algorithm based on wavelet domain hierarchical Markov model is proposed.The new image model is described as wavelet coefficient joint distribution with finite general mixture model(FGM),while the GMM in HMT model is only one of the FGMs.Vitilizing on the local interactions of labels described by Markov random field(MRF),the label field priori probability model with explicit expression,which overcomes the shortcoming of the independence between labels in the HMT model,is determined.Using Bayes principle,the recursive algorithm of image segmentation is derived.The proposed model inherits not only the characteristics of spatial domain hierarchical MRF model with effective recursive algorithm but also the characteristics of HMT model with the variable Markov parameters in different scales.The experiments with real images and synthetic texture images are carried out,the results show that the proposed method outperforms other standard segmentation methods,such as accurately locating image edges,correctly identifying different regions.