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Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

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Abstract

Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed “sample optimization problem.” In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversity of population. Besides, a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting.

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Funding

This study was funded by National Natural Science Foundation of China (61370102, 61170193, 61370185), Guangdong Natural Science Foundation (2014A030306050, S2012010009865, s2013010013432, S2013010015940), the Fundamental Research Funds for the Central Universities, SCUT (2015PT022), Science and Technology Planning Project of Huizhou City (2011P002, 2011g012, 2011P005, 2011P003, 2011g011, 2013B020015008) and Science and Technology Planning Project of Guangdong Province (2011B090400041, 2012B010100039, 2012B040305011, 2012B010100040, 2015B010129015). Education and Science Programs of Guangdong Province (11JXZ012, 14JXN065), Discipline Construction Programs of Guangdong Province (2013LYM00874), Key Technology Research and Development Programs of Huizhou (2013-13, 2013B020015008, 2014B050013016), Science and Technology Plan Project of Huizhou University (2012QN09), Distinguished Young Scholars Fund of Department of Education (No. Yq2013126).

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Authors and Affiliations

  1. College of Huizhou, Huizhou, 516007, China

    Zhao-Quan Cai & Hui Hu

  2. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Liang Lv, Han Huang & Yi-Hui Liang

Authors
  1. Zhao-Quan Cai
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  2. Liang Lv
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  3. Han Huang
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  4. Hui Hu
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  5. Yi-Hui Liang
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Correspondence to Liang Lv.

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All authors of this paper declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Cai, ZQ., Lv, L., Huang, H. et al. Improving sampling-based image matting with cooperative coevolution differential evolution algorithm. Soft Comput 21, 4417–4430 (2017). https://doi.org/10.1007/s00500-016-2250-7

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