IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-030-70281-6_15.html
   My bibliography  Save this book chapter

Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation

In: Applying Particle Swarm Optimization

Author

Listed:
  • Sajad Ahmad Rather

    (Pondicherry University)

  • P. Shanthi Bala

    (Pondicherry University)

Abstract

Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. In this work, a new image segmentation method has been introduced based on the constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image histogram act as searcher agents of the CPSOGSA. Besides, the optimal number of thresholds is determined using Kapur’s entropy method. The effectiveness and applicability of CPSOGSA have been accomplished by applying it to four standard images from the USC-SIPI image database including airplane, cameraman, clock, and truck. Various performance metrics have been employed to investigate the simulation outcomes including optimal thresholds, standard deviation, mean, run-time analysis, PSNR (peak signal-to-noise ratio), best fitness value calculation, convergence maps, and box plot analysis. In addition, the experimental results of CPSOGSA are compared with standard PSO and GSA. The simulation results clearly indicate that hybrid CPSOGSA takes less computational time in finding the best threshold values of the benchmark images.

Suggested Citation

  • Sajad Ahmad Rather & P. Shanthi Bala, 2021. "Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation," International Series in Operations Research & Management Science, in: Burcu Adıgüzel Mercangöz (ed.), Applying Particle Swarm Optimization, edition 1, chapter 0, pages 279-305, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-70281-6_15
    DOI: 10.1007/978-3-030-70281-6_15
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:isochp:978-3-030-70281-6_15. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.