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

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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.

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Acknowledgments

This work was supported partially by the National Natural Science Foundation of China (Nos. 61472171 & 61701212), Key Scientific Research Project of Liaoning Provincial Education Department (LZ2019001), Natural Science Foundation of Liaoning Province (2019-ZD-0468).

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Pan-pan Niu, Li Wang, Xin Shen, Qian Wang & Xiang-yang Wang

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  1. Pan-pan Niu
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  2. Li Wang
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  3. Xin Shen
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  4. Qian Wang
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  5. Xiang-yang Wang
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Correspondence to Pan-pan Niu or Xiang-yang Wang.

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Niu, Pp., Wang, L., Shen, X. et al. Texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase. Multimed Tools Appl 79, 29799–29824 (2020). https://doi.org/10.1007/s11042-020-09491-4

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  • DOI: https://doi.org/10.1007/s11042-020-09491-4

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