IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i19p2383-d642832.html
   My bibliography  Save this article

Automatic Superpixel-Based Clustering for Color Image Segmentation Using q-Generalized Pareto Distribution under Linear Normalization and Hunger Games Search

Author

Listed:
  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Esraa Osama Abo Zaid

    (Department of Mathematics, Faculty of Science, Seuz University, Suez 41522, Egypt
    Academy of Scientic Research and Technology (ASRT) of the Arab Republic of Egypt, Cairo 11516, Egypt)

  • Mohammed A. A. Al-qaness

    (State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Rehab Ali Ibrahim

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics.

Suggested Citation

  • Mohamed Abd Elaziz & Esraa Osama Abo Zaid & Mohammed A. A. Al-qaness & Rehab Ali Ibrahim, 2021. "Automatic Superpixel-Based Clustering for Color Image Segmentation Using q-Generalized Pareto Distribution under Linear Normalization and Hunger Games Search," Mathematics, MDPI, vol. 9(19), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2383-:d:642832
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/19/2383/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/19/2383/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. EL-Latif, Ahmed A. Abd & Abd-El-Atty, Bassem & Venegas-Andraca, Salvador E., 2020. "Controlled alternate quantum walk-based pseudo-random number generator and its application to quantum color image encryption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. Mohamed Abd Elaziz & Mohammed A. A. Al-qaness & Esraa Osama Abo Zaid & Songfeng Lu & Rehab Ali Ibrahim & Ahmed A. Ewees, 2021. "Automatic clustering method to segment COVID-19 CT images," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-13, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pai Liu & Shihua Zhou & Wei Qi Yan, 2022. "A 3D Cuboid Image Encryption Algorithm Based on Controlled Alternate Quantum Walk of Message Coding," Mathematics, MDPI, vol. 10(23), pages 1-26, November.
    2. Ahmed A. Abd El-Latif & Janarthanan Ramadoss & Bassem Abd-El-Atty & Hany S. Khalifa & Fahimeh Nazarimehr, 2022. "A Novel Chaos-Based Cryptography Algorithm and Its Performance Analysis," Mathematics, MDPI, vol. 10(14), pages 1-22, July.
    3. Gao, Ya-jun & Xie, Hong-wei & Zhang, Jun & Zhang, Hao, 2022. "A novel quantum image encryption technique based on improved controlled alternated quantum walks and hyperchaotic system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    4. Haider, Muhammad Imran & Shah, Tariq & Ali, Asif & Shah, Dawood & Khalid, Ijaz, 2023. "An Innovative approach towards image encryption by using novel PRNs and S-boxes Modeling techniques," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 153-168.
    5. Xianhua Song & Guanglong Chen & Ahmed A. Abd El-Latif, 2022. "Quantum Color Image Encryption Scheme Based on Geometric Transformation and Intensity Channel Diffusion," Mathematics, MDPI, vol. 10(17), pages 1-23, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2383-:d:642832. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.