Multilevel Thresholding based Image Segmentation using Whale Optimization Algorithm
Basu Dev Shivahare1, S.K.Gupta2

1Basu Dev Shivahare*,Research Scholar, AKTU, Lucknow, Uttar Pradesh, India.
2S.K.Gupta , Associate Professor, BIET, Jhansi ,Uttar Pradesh, India.

Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4602-4613 | Volume-8 Issue-12, October 2019. | Retrieval Number: L38431081219/2019©BEIESP | DOI: 10.35940/ijitee.L3843.1081219
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Abstract: Whale Optimization Algorithm (WOA) was proposed by Seyedali Mirjalili and Andrew Lewis in 2016. WOA is nature-inspired, meta-heuristic (randomization and deterministic) algorithm, which is being used to solve various single objective, multi objective and multi-dimensional optimization problems. To determine threshold value for image segmentation Otsu, kapur, thresholding etc. methods are used. In this paper multilevel threshold values are computed using WOA and these multilevel threshold values are used for image segmentation. Fitness is computed using Otsu thresholding. Minimum fitness score is considered as best optimal value. WOA has capability to explore, exploit the search s pace and avoid local optima. In multilevel thresholding, complex images are segmented into L+1 levels for multiple threshold values L =2, 3 etc. This paper addresses about performance of Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for various benchmark objective functions such as unimodel, multimodel, fix dimension multimodel based on their convergence curves for different number of iterations400,500 600 and compute multilevel threshold values for various level image segmentation using Whale Optimization Algorithm.
Keywords: Nature Inspired Algorithm, Whale Optimization Algorithm (WOA), Multilevel Thresholding, Image Segmentation
Scope of the Article: Algorithm Engineering