research-article

Using Particle Swarm Large-scale Optimization to Improve Sampling-based Image Matting

Authors:

Hu HuiAuthors Info & Claims

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 957 - 961

https://doi.org/10.1145/2739482.2768444

Published: 11 July 2015 Publication History

Abstract

Sampling-based image matting is an important basic operator of image processing. The matting results are depended on the quality of sample selection. The sample selection produces a pair of samples for each pixel to detect whether the pixel is in the foreground of an image. Therefore, how to optimize the production is usually modeled as a large-scale optimization problem. In this study, particle swarm optimization is applied to solve the problem because its property of rapid convergence is positive to the real-time demand of image matting. We regard every two dimensions of a particle as a sample pair for a undetermined pixel. The encoding can make image matting more effective when there are relevant pixels in the image. The experimental result indicates that the proposed particle swarm optimization performs better than existing optimization method for image matting.

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Cited By

Liang YGou HLiu GHuang S(2023)Fast Image matting based on matting pyramids and surrogate models2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)10.1109/RAIIC59453.2023.10280926(387-394)Online publication date: 11-Aug-2023
https://doi.org/10.1109/RAIIC59453.2023.10280926
Liang YGou HFeng FLiu GHuang H(2023)Natural image matting based on surrogate model▪Applied Soft Computing10.1016/j.asoc.2023.110407143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110407
Liu SFeng FGou HZhou ZTan MWang L(2021)Grouping optimization algorithm for natural image matting2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI)10.1109/CEI52496.2021.9574550(421-426)Online publication date: 24-Sep-2021
https://doi.org/10.1109/CEI52496.2021.9574550
Show More Cited By

Index Terms

Using Particle Swarm Large-scale Optimization to Improve Sampling-based Image Matting
1. Computing methodologies
  1. Artificial intelligence
  2. Computer graphics
    1. Image manipulation
      1. Image processing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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Funding Sources

The Pearl River Science&technology Star Project
National Natural Science Foundation of China
the Ministry of Education - China Mobile Research Funds
the Fundamental Research Funds for the Central Universities, SCUT
Guangdong Natural Science Foundation

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Liang YGou HLiu GHuang S(2023)Fast Image matting based on matting pyramids and surrogate models2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)10.1109/RAIIC59453.2023.10280926(387-394)Online publication date: 11-Aug-2023
https://doi.org/10.1109/RAIIC59453.2023.10280926
Liang YGou HFeng FLiu GHuang H(2023)Natural image matting based on surrogate model▪Applied Soft Computing10.1016/j.asoc.2023.110407143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110407
Liu SFeng FGou HZhou ZTan MWang L(2021)Grouping optimization algorithm for natural image matting2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI)10.1109/CEI52496.2021.9574550(421-426)Online publication date: 24-Sep-2021
https://doi.org/10.1109/CEI52496.2021.9574550
Liang YHuang HCai Z(2020)PSO-ACSC: a large-scale evolutionary algorithm for image mattingFrontiers of Computer Science10.1007/s11704-019-8441-514:6Online publication date: 6-May-2020
https://doi.org/10.1007/s11704-019-8441-5

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