research-article

Image Segmentation Technology Based on Genetic Algorithm

Authors:

Fei ZengAuthors Info & Claims

ICDSP '19: Proceedings of the 2019 3rd International Conference on Digital Signal Processing

Pages 27 - 31

https://doi.org/10.1145/3316551.3318229

Published: 24 February 2019 Publication History

Abstract

Image segmentation technology is one of the important topics in the field of digital image research. However, there is no uniform standard for existing image segmentation methods, and the traditional image segmentation method is only suitable for some specific occasions. Therefore, it is very urgent to research and develop new theories and methods of image segmentation technology. Genetic algorithm is a method for calculating the optimal solution by simulating the biological evolution process in the natural selection and genetic mechanism of biological evolution. It has strong robustness, parallelism, adaptability and fast convergence. It can be applied in image segmentation technology to determine the segmentation threshold. Therefore, this paper studies the image segmentation based on genetic algorithm, and compares different image segmentation algorithms. The experimental results show that the image segmentation effect based on genetic algorithm is better than the traditional image segmentation.

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Cited By

Jiang JKim DKim BShin Y(2025)GA-VAE: Enhancing Local Feature Representation in VQ-VAE Through Genetic Algorithm-Based Token OptimizationIEEE Access10.1109/ACCESS.2025.354238413(34286-34295)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3542384
Namiranian FLatif A(2024)A New Approach for Digital Image Segmentation with Genetic Algorithm and Random ForestSignal and Data Processing10.61186/jsdp.20.4.3520:4(35-44)Online publication date: 1-Mar-2024
https://doi.org/10.61186/jsdp.20.4.35
Zhang YLi YChen JYang CRolfe P(2023)Fine‐Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target SegmentationInternational Journal of Intelligent Systems10.1155/2023/28503702023:1Online publication date: 20-Apr-2023
https://doi.org/10.1155/2023/2850370
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Index Terms

Image Segmentation Technology Based on Genetic Algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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cover image ACM Other conferences

ICDSP '19: Proceedings of the 2019 3rd International Conference on Digital Signal Processing

February 2019

170 pages

ISBN:9781450362047

DOI:10.1145/3316551

Copyright © 2019 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 the author(s) 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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New York, NY, United States

Publication History

Published: 24 February 2019

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ICDSP 2019

ICDSP 2019: 2019 3rd International Conference on Digital Signal Processing

February 24 - 26, 2019

Jeju Island, Republic of Korea

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Cited By

Jiang JKim DKim BShin Y(2025)GA-VAE: Enhancing Local Feature Representation in VQ-VAE Through Genetic Algorithm-Based Token OptimizationIEEE Access10.1109/ACCESS.2025.354238413(34286-34295)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3542384
Namiranian FLatif A(2024)A New Approach for Digital Image Segmentation with Genetic Algorithm and Random ForestSignal and Data Processing10.61186/jsdp.20.4.3520:4(35-44)Online publication date: 1-Mar-2024
https://doi.org/10.61186/jsdp.20.4.35
Zhang YLi YChen JYang CRolfe P(2023)Fine‐Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target SegmentationInternational Journal of Intelligent Systems10.1155/2023/28503702023:1Online publication date: 20-Apr-2023
https://doi.org/10.1155/2023/2850370
Dong WChen YHu X(2022)Image Multithreshold Segmentation Method Based on Improved Harris Hawk OptimizationMathematical Problems in Engineering10.1155/2022/74010402022(1-16)Online publication date: 5-May-2022
https://doi.org/10.1155/2022/7401040
Verma SShukla RDutta G(2022)Machine Learning-Enabled Biosensors in Clinical Decision MakingNext-Generation Nanobiosensor Devices for Point-Of-Care Diagnostics10.1007/978-981-19-7130-3_7(163-194)Online publication date: 3-Dec-2022
https://doi.org/10.1007/978-981-19-7130-3_7
Deshpande NGite SPradhan BKotecha KAlamri A(2021)Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of LeukemiaMathematical Biosciences and Engineering10.3934/mbe.202209319:2(1970-2001)Online publication date: 2021
https://doi.org/10.3934/mbe.2022093
Huang YXiong LLiu YDeng PDan B(2021)Image segmentation of argon blowing based on improved Otsu algorithm2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS)10.1109/ICoIAS53694.2021.00017(49-54)Online publication date: May-2021
https://doi.org/10.1109/ICoIAS53694.2021.00017
Bhattacharjee JSantra SDeyasi A(2021)A Metaheuristic Approach for Image Segmentation Using Genetic AlgorithmAdvances in Smart Communication Technology and Information Processing10.1007/978-981-15-9433-5_13(125-134)Online publication date: 16-Feb-2021
https://doi.org/10.1007/978-981-15-9433-5_13

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