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A Robust Matting Method Combined with Sparse-Coded Model

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Business Intelligence and Information Technology (BIIT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 107))

Abstract

Digital image matting is widely used in virtual reality, augmented reality, and film and television production. It is mainly used to solve the problem of separating foreground information from background information. Specifically, it is to solve the value of the unknown pixels between foreground and background information. Digital image matting methods mainly include sampling method and affine method, this paper proposes a sampling method that combines Sparse-Coded and Robust matting algorithm, named SRMatting. The robust algorithm uses a local sparse single-point sampling method at the edge. Sparse-Coded sampling has a lot less color redundancy than dense sampling. Still, local edge sampling has the problem of insufficient depth and breadth color information collection. Therefore, this article combines the Sparse-Coded algorithm (global depth sparse method) with the sampling part of the Robust algorithm to enrich the diversity of samples and adds the preprocessing steps to the new algorithm SRMatting to improve the accuracy of the solution and reduce the amount of calculation.

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Acknowledgment

This work is supported by the Youth Innovation Talent Support Program of Harbin University of Commerce (No. 2020CX39).

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Authors and Affiliations

  1. Harbin University of Commerce, Harbin, 150028, China

    Guilin Yao, Huixin Yang & Shirui Wu

  2. Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin, 150028, China

    Guilin Yao, Huixin Yang & Shirui Wu

Authors
  1. Guilin Yao
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  2. Huixin Yang
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  3. Shirui Wu
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Corresponding author

Correspondence to Guilin Yao .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Yaoqun Xu

  3. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Zhijie Zhao

  4. Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada

    Sabah Mohammed

  5. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Zhipeng Fan

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Yao, G., Yang, H., Wu, S. (2022). A Robust Matting Method Combined with Sparse-Coded Model. In: Hassanien, A.E., Xu, Y., Zhao, Z., Mohammed, S., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-030-92632-8_12

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