Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

Expert Systems - Tập 38 Số 7 - 2021
Sajad Ahmad Rather1, P. Shanthi Bala1
1Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India

Tóm tắt

AbstractImage 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. 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. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.

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Tài liệu tham khảo

10.1016/j.eswa.2017.04.023

10.1016/j.eswa.2019.01.075

Abdel‐Basset M., 2020, A novel equilibrium optimization algorithm for multi‐thresholding image segmentation problems, Neural Computing and Applications, 1

10.1016/j.knosys.2019.105237

10.1016/j.dsp.2013.07.005

10.1016/j.ast.2020.105783

Chen K., 2016, Multilevel image segmentation based on an improved firefly algorithm, Mathematical Problems in Engineering, 2016, 1

10.1109/4235.985692

10.1016/j.swevo.2011.02.002

10.1007/s13042-020-01189-1

10.1007/978-3-540-78987-1_21

10.1109/TIM.2009.2030931

10.1007/s00170-018-2543-3

10.1016/j.asoc.2020.106063

10.1016/j.eswa.2009.12.050

10.1049/el:20080522

10.1007/s00034-018-0993-3

10.1016/0734-189X(85)90125-2

10.1007/978-3-540-72950-1_77

Kennedy J. &Eberhart R.(1995).Particle swarm optimization. In Proceedings of ICNN'95‐IEEE International Conference on Neural Networks (pp. 1942–1948).

10.1016/j.eswa.2017.04.029

10.3139/120.111478

10.3233/HIS-2004-13-403

10.1016/j.ijleo.2019.02.004

10.1109/ACCESS.2019.2891673

10.1016/j.eswa.2007.01.002

10.1016/j.knosys.2015.12.022

10.1016/j.advengsoft.2013.12.007

10.1016/j.advengsoft.2017.07.002

Mirjalili S. &Hashim S. Z. M.(2010).A new hybrid PSOGSA algorithm for function optimization. In 2010 international conference on computer and information application (pp. 374‐377). IEEE.

10.1016/j.knosys.2015.07.006

10.1049/iet-ipr.2016.0489

10.5566/ias.v33.p65-74

Mozaffari M. H. &Lee W. S.(2016).Multilevel thresholding segmentation of T2 weighted brain MRI images using convergent heterogeneous particle swarm optimization. arXiv preprint arXiv:1605.04806.

Mozaffari M. H., 2016, IPO: An inclined planes system optimization algorithm, Computing and Informatics, 35, 222

Mozaffari M. H. Abdy H. &Zahiri S. H.(2013).Application of inclined planes system optimization on data clustering. In 2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) (pp. 1–3). IEEE.

10.1155/2013/575414

10.1016/j.neucom.2014.02.020

10.1109/TSMC.1979.4310076

10.3139/120.111509

10.3139/120.111529

10.1016/j.imavis.2010.08.009

Patel V., 2020, Qualitative and quantitative performance comparison of recent optimization algorithms for economic optimization of the heat exchangers, Archives of Computational Methods in Engineering, 1

10.1016/j.ins.2009.03.004

10.1108/IJICC-09-2019-0105

10.1108/WJE-09-2019-0254

Rather S. A. &Bala P. S.(2019a).A holistic review on gravitational search algorithm and its hybridization with other algorithms. In 2019 IEEE International Conference on Electrical Computer and Communication Technologies (ICECCT) (pp. 1–6).

Rather S. A. &Bala P. S.(2019b).Analysis of gravitation based optimization algorithms for clustering and classification. In Handbook of research on big data clustering and machine learning (pp. 77–99). IGI Global.

Rather S. A. &Bala P. S.(2019c).Hybridization of constriction coefficient based Particle Swarm Optimization and Gravitational Search Algorithm for function optimization. In 2019 Elsevier International Conference on Advances in Electronics Electrical and Computational Intelligence (ICAEEC‐2019).

Rather S. A., 2019, International Conference on Advanced Communication and Networking, 95

Resma K. B., 2018, Multilevel thresholding for image segmentation using Krill Herd optimization algorithm, Journal of King Saud University‐Computer and Information Sciences

10.4236/jcc.2019.73002

10.1504/IJVD.2019.109864

10.1016/j.eswa.2015.11.016

10.1016/j.eswa.2011.06.004

10.1109/ACCESS.2018.2837062

10.1016/j.asoc.2016.01.054

Tuba E. Alihodzic A. &Tuba M.(2017).Multilevel image thresholding using elephant herding optimization algorithm. In 2017 14th international conference on engineering of modern electric systems (EMES) (pp. 240–243). IEEE.

Tuba M. Bacanin N. &Alihodzic A.(2015).Multilevel image thresholding by fireworks algorithm. In 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 326–330). IEEE.

Upadhyay P., 2019, Kapur's entropy based optimal multilevel image segmentation using crow search algorithm, Applied Soft Computing, 97, 1

10.1109/TIP.2003.819861

Wilcoxon F., 1945, Individual comparisons by ranking methods. Biometrics, Bulletin, 1, 80

10.1109/ACCESS.2019.2896673

10.3139/120.111153

10.3139/120.111541

10.3139/120.111479

10.3139/120.111494

10.3139/120.111511

10.3139/120.111495

10.3139/120.111492

10.1007/s00170-019-04532-1

10.3139/120.111378

10.1007/s11831-019-09343-x