CPSOGSA for Multilevel Image Thresholding

CPSOGSA is employed to find the optimal pixels in the benchmark images

This work introduces a new image segmentation method based on the constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC-SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate.

This is the source code of the paper:

Rather, S. A., & Bala, P. S. (2021), “Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Multilevel Image Thresholding”, Expert Systems, https://doi.org/10.1111/exsy.12717, Wiley, SCIE (I.F = 2.587).

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