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A Markov Random Field Model-Based Approach to Natural Image Matting

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Abstract

This paper proposes a Markov Random Field (MRF) model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori (MAP) estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.

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Authors and Affiliations

  1. Department of Communicative Technology, Zhejiang Institute of Media and Communication, Hangzhou, 310018, China

    Sheng-You Lin

  2. State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, 310058, China

    Jiao-Ying Shi

Authors
  1. Sheng-You Lin
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  2. Jiao-Ying Shi
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Correspondence to Sheng-You Lin.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 60033010, Zhejiang Provincial Natural Science Foundation of China under Grant No. Y105324 and Planned Program of Science and Technology Department of Zhejiang Province, China (Grant No. 2006C31065).

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Lin, SY., Shi, JY. A Markov Random Field Model-Based Approach to Natural Image Matting. J Comput Sci Technol 22, 161–167 (2007). https://doi.org/10.1007/s11390-007-9022-x

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  • DOI: https://doi.org/10.1007/s11390-007-9022-x

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