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VBI-MRF model for image segmentation

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Abstract

In statistical image segmentation, the distribution of pixel values is usually assumed to be Gaussian and the optimal result is believed to be the one that has maximum a posteriori (MAP) probability. In spite of its prevalence and computational efficiency, the Gaussian assumption, however, is not always strictly followed, and hence may lead to less accurate results. Although the variational Bayes inference (VBI), in which statistical model parameters are also assumed to be random variables, has been widely used, it can hardly handle the spatial information embedded in pixels. In this paper, we incorporate spatial smoothness constraints on pixels labels interpreted by the Markov random field (MRF) model into the VBI process, and thus propose a novel statistical model called VBI-MRF for image segmentation. We evaluated our algorithm against the variational expectation-maximization (VEM) algorithm and the hidden Markov random field (HMRF) model and MAP-MRF model based algorithms on both noise-corrupted synthetic images and mosaics of natural texture. Our pilot results suggest that the proposed algorithm can segment images more accurately than other three methods and is capable of producing robust image segmentation.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61471297, in part by the Natural Science Foundation of Shaanxi Province, China, under Grant 2015JM6287, and in part by the Returned Overseas Scholar Project of Shaanxi Province, China.

We appreciate the efforts devoted by the authors of the Brodatz album and the MSRC-21 dataset to collect and share the data for comparing texture description and image segmentation algorithms.

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Authors and Affiliations

  1. Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Yong Xia & Zhe Li

  2. Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Yong Xia & Zhe Li

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  1. Yong Xia
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  2. Zhe Li
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Correspondence to Yong Xia.

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Xia, Y., Li, Z. VBI-MRF model for image segmentation. Multimed Tools Appl 77, 13343–13361 (2018). https://doi.org/10.1007/s11042-017-4951-z

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