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PSO-ACSC: a large-scale evolutionary algorithm for image matting

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Abstract

Image matting is an essential image processing technology due to its wide range of applications. Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs. It is essentially a large-scale multi-peak optimization problem of pixel pairs. Previous study shows that particle swarm optimization (PSO) can effectively optimize the pixel pairs. However, it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima. To address this problem, this work presents a parameter-free strategy for PSO called adaptive convergence speed controller (ACSC). ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator (CPPRO) and pixel pair reset operator (PPRO) during the iteration. ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution. PPRO is performed to avoid premature convergence when the alpha mattes regarding two selected particles are highly similar. Experimental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61772225, 61876207, 61502088), National Key R&D Program of China (2018YFC0823803, 2018YFC0823802), Zhongshan Science and Technology Research Project of Social welfare (2019B2010), Guangdong Natural Science Funds for Distinguished Young Scholar (2014A030306050), Guangdong Highlevel personnel of special support program (2014TQ01X664), International Cooperator Project of Guangzhou (201807010047), National Natural Science Foundation of Guangdong (2018B030311046), Guangdong University Key Platforms and Research Projects (2018KZDXM066, 2017KZDXM081, 2015KQNCX153), Guangzhou Science and Technology Projects (201802010007, 201804010276) and Youth science and technology talents cultivating object of Guizhou province (Qian education cooperation KY word [2016]165).

Author information

Authors and Affiliations

  1. School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, 528400, China

    Yihui Liang

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

    Han Huang

  3. Research Department, Huizhou University, Huizhou, 516007, China

    Zhaoquan Cai

Authors
  1. Yihui Liang
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  2. Han Huang
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  3. Zhaoquan Cai
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Corresponding author

Correspondence to Han Huang.

Additional information

Yihui Liang received the BS degree in digital media technology from Xi’an University of Technology, China in 2012, the MEng degree and the PhD degree in software engineering from South China University of Technology, China in 2015 and 2019, respectively. He is currently a lecturer with School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, China. His current research interests include alpha matting, image processing.

Han Huang received the BMan degree in information management and information system from School of Mathematics, South China University of Technology, China in 2003, and the PhD degree in computer science from the South China University of Technology (SCUT), China in 2008. Currently, he is a professor with the School of Software Engineering in SCUT, China. His research interests include evolutionary computation, and swarm intelligence and their application. Dr. Huang is a senior member of CCF and a member of IEEE.

Zhaoquan Cai, professor, CCF member (E2006137S), received the bachelor degree in Computer Science and Technology from South China University of Technology in 1998, and the master degree in Computer Science and Technology from Huazhong University of Science and Technology, China in 2006. He is currently the director of the Science Research Management Department in Huizhou University and a member of China Computer Federation. His current research interests mainly focus on computer networks, intelligent computing and database.

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Liang, Y., Huang, H. & Cai, Z. PSO-ACSC: a large-scale evolutionary algorithm for image matting. Front. Comput. Sci. 14, 146321 (2020). https://doi.org/10.1007/s11704-019-8441-5

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  • DOI: https://doi.org/10.1007/s11704-019-8441-5

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