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

Sajad Ahmad Rather and P. Shanthi Bala
Additional contact information
Sajad Ahmad Rather: Pondicherry University
P. Shanthi Bala: Pondicherry University

Chapter Chapter 15 in Applying Particle Swarm Optimization, 2021, pp 279-305 from Springer

Abstract: 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.

Keywords: CPSOGSA; Image segmentation; Multilevel thresholding; Optimization; Kapur’s entropy method; Particle swarm optimization (PSO); Hybridization; Constriction coefficient; Meta-heuristics; Optimization; Gravitational search algorithm (GSA) (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-70281-6_15

Ordering information: This item can be ordered from
http://www.springer.com/9783030702816

DOI: 10.1007/978-3-030-70281-6_15

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().