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Alpha matting with image pixel correlation

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International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Alpha matting aims at estimating the foreground opacity matte in an image. It is critical to find the best known samples for foreground and background color of unknown pixels in color sampling-based matting approaches. Most matting approaches can only select color sample for each pixel each time, which prevent them from maintaining the same correlation in image pixels. In particular, they may fail to collect appropriate samples from complex images and thus lead to artifacts. In order to solve the problem, we present a correlation-based sampling method in which the image pixel correlation is employed in color sampling and optimal sample selection for the alpha matte estimation of the image. First, the foreground and background colors of sample set can completely cover the color of unknown pixels to avoid missing the true samples. This is accomplished by artificial immune network adaptively learning the image correlation in unknown pixels. Besides, we propose the sample selection process as a global optimization problem with image correlation. All unknown pixels are treated as a high-dimensional input variable, particle swarm optimization algorithms is employed to solve the global optimization problem selecting the best sample pairs for all unknown pixels. The experimental study on images dataset shows that image pixels correlation is effective to improve matting, and that our matting results are comparable to some recent approaches.

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Acknowledgments

This work is financially supported by NSFC-Guangdong Joint Found (U1501254), Natural Science Foundation of China  (61370102, 61202269, 61472089, 61572143, 61502108, 61502109), Natural Science Foundation of Guangdong province (2014A030306050, 2014A030306004, 2014A030308008) Key Technology Research and Development Programs of Guangdong Province (2012B01010029, 2013B051000076, 2015B010108006, 2015B010131015), Science and Technology Plan Project of Guangzhou City (2014Y2-00027), Opening Project of the State Key Laboratory for Novel Software Technology (KFKT2014B03, KFKT2014B23) the Fundamental Research Funds for the Central Universities, SCUT (2015PT022), Philosophy and social science project of Guangdong Provenience (GD14XYJ24) and Guangdong High-level personnel of special support program (2014TQ01X664).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, People’s Republic of China

    Xueming Yan

  2. School of Mathematics and Big Data, Foshan University, Foshan, 510006, People’s Republic of China

    Zhifeng Hao

  3. School of Software Engineering, South China University of Technology, Guangzhou, 510006, People’s Republic of China

    Han Huang

Authors
  1. Xueming Yan
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  2. Zhifeng Hao
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  3. Han Huang
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Correspondence to Han Huang.

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Yan, X., Hao, Z. & Huang, H. Alpha matting with image pixel correlation. Int. J. Mach. Learn. & Cyber. 9, 621–627 (2018). https://doi.org/10.1007/s13042-016-0584-1

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  • DOI: https://doi.org/10.1007/s13042-016-0584-1

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