Texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase

Abstract

Texture segmentation is a frequently occurring and challenging problem in many computer vision and pattern recognition applications. The importance of phase information for texture analysis has been earlier established for many image processing. Undecimated dual tree complex wavelet transform (UDTCWT) is a new image decomposition. It not only provides exact translational invariance and rich directional selectivity, but also offers perfect consistent relative phase relationships across scales. In this paper, we propose a novel texture image segmentation framework using Vonn mixtures-based hidden Markov trees (HMT) and UDTCWT domain relative phase. Firstly, we analyze the robustness and marginal distribution of UDTCWT relative phases, and various strong dependencies between UDTCWT relative phases. Then, we propose a new HMT statistical model in UDTCWT domain, namely Vonn mixtures-based HMT, by describing the UDTCWT relative phases statistical distribution with Vonn mixtures (VM), which can capture both the subband marginal distributions and the strong dependencies across scales of the UDTCWT relative phases. Finally, we develop a texture image segmentation framework using the Vonn mixtures-based HMT model of UDTCWT domain relative phases, in which expectation–maximization (EM) parameter estimation, Bayesian multiscale raw segmentation, and context based multiscale fusion are used. Comparing to the state-of-the-art techniques, the proposed method can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.