Multiscale texture segmentation via a contourlet contextual hidden Markov model

The contourlet transform is an emerging multiscale multidirection image processing technique. It effectively represents smooth curvature details typical of natural images, overcoming a major drawback of the 2-D wavelet transform. Previously, we developed a contourlet image model, that is, the contourlet contextual hidden Markov model (C-CHMM). In this paper, we further develop a multiscale texture segmentation technique based on the C-CHMM. The segmentation method combines a model comparison approach with a multiscale fusion and a neighbor combination process. It also features a neighborhood selection scheme based on smoothed context maps, for both model estimation and neighbor combination. Through a series of segmentation experiments, we examine the effectiveness of the C-CHMM in comparison with closely related models. We also investigate how different context designs affect the segmentation performance. Moreover, we show that the C-CHMM based technique provides improved accuracy in segmenting texture patterns of diversified nature, as compared with popular methods such as the HMTseg and the JMCMS. All these simulation experiments demonstrate the great potential of the C-CHMM for image analysis applications.