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BM-GMM: belief function-based Gaussian Markov model for image segmentation

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Abstract

The Gaussian mixture model (GMM) is an efficient statistical tool successfully used in the computer vision community. In this paper, we expand the classical GMM to a novel model based on Markov random field (MRF) in the belief function framework for image segmentation tasks, named BM-GMM. The main advantage of BM-GMM is that it provides a convenient way to cluster data and requires a few parameters for optimization. Moreover, the proposed BM-GMM considers the spatial relationship between pixels via incorporating MRF to improve the robustness of the model against noise. The evidential membership allows the object to belong to the individual clusters and the meta-clusters composed of several collections. Therefore, the belief function theory has improved the GMM’s reliability for uncertainty analysis. Experimental results under various image datasets consistently demonstrated the proposed BM-GMM robustness, accuracy, and effectiveness.

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Data Availibility Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/.

  2. http://www.isles-challenge.org/ISLES2015/.

  3. https://github.com/kammnd/MRI-knee-dataset.

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Authors and Affiliations

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Tong Hou & Hongqing Zhu

  2. Department of Mathematics, Natural, Mathematical & Engineering Sciences, King’s College London, WC2R 2LS, London, UK

    Suyi Yang

Authors
  1. Tong Hou
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  2. Hongqing Zhu
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  3. Suyi Yang
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Tong Hou and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hongqing Zhu.

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H. Zhu: This work was supported by the National Nature Science Foundation of China under Grant 61872143.

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Hou, T., Zhu, H. & Yang, S. BM-GMM: belief function-based Gaussian Markov model for image segmentation. SIViP 17, 4551–4560 (2023). https://doi.org/10.1007/s11760-023-02690-0

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  • DOI: https://doi.org/10.1007/s11760-023-02690-0

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