Kapur’s Entropy for Underwater Multilevel Thresholding Image Segmentation Based on Whale Optimization Algorithm

Abstract

Multilevel thresholding is an effective and indispensable technology for image segmentation that has attracted extensive attention in recent years. However, the multilevel thresholding method has some disadvantages, such as a large computational complexity and low segmentation accuracy. Therefore, this paper proposes a whale optimization algorithm (WOA) based on Kapur's entropy method to solve the image segmentation problem. The WOA can effectively balance exploration and exploitation to avoid falling into premature convergence and obtain the global optimal solution. To verify the segmentation performance of the WOA, a series of experiments on underwater images from the experimental pool of Harbin Engineering University are conducted, and the segmentation results are compared with those of the BA, the FPA, MFO, the MSA, PSO and WWO by maximizing the fitness value of Kapur's entropy method. The fitness value, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), execution time and Wilcoxon's rank-sum test are used to evaluate the overall performance of each algorithm. The experimental results reveal that the WOA is superior to the other comparison algorithms and has a higher segmentation accuracy, better segmentation effect and stronger robustness. In addition, the feasibility and efficiency of the WOA are verified.

Threshold image segmentation based on improved sparrow search algorithm

“…Nejad et al proposed a multi-level thresholding method based on imperialist competition algorithm and Kapur’s entropy [ 40 ]. Yan et al proposed a image segmentation method based on whale optimization algorithm (WOA) and Kapur’s entropy [ 57 ]. Lei et al defined a new form of square rough entropy to measure the roughness in an image, and proposed a corresponding threshold image segmentation algorithm [ 23 ].…”

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

“…FSIM and SSIM The proposed ADMM shows some better properties such as faster calculation speed (the request time was under 0.1 s) and better stability 47 Crow Search Algorithm (CSA) Kapur’s entropy Upadhyay and Chhabra ( 2020 ) Standard Gray Scale Images Proposed method is compared with PSO, DE, GWO, MFO andCS PSNR, FSIM and SSIM The proposed CSA showcase high robustness, accuracy, efficiency, suitability and search capability when compared with other mentioned algorithms. Further, proposed method could be suitable to be applied over multispectral satellite images and moreover to focus on multi-objective optimization problems 48 Whale Optimization Algorithm (WOA) Kapur’s entropy Yan et al ( 2020 ) Gray Scale Images Proposed method is compared with BA, FPA, MFO, MSA, PSO andWWO The fitness value, PSNR, SSIM and execution time The proposed WOA has the ability to explore and further navigate between the global and local search with the sole intention to obtain the optimum solution to the given problem. Further, the same is projected using the experimental results showing its better performance in terms of convergence speed and quality of segmentation 49 Bat Algorithm (BA 1 ) Kapur’s entropy Dey et al ( 2021 ) Standard Color and Gray Scale Images Proposed method is compared with BA2, BA3, BA4 and BA5 PSNR, NCC, AD, SC and NAE Though the average IQPs attained using all BA methods are identical.…”

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

“…Among them, Otsu’s technique maximizes the between-class variance of each segmented class to achieve the optimal thresholds [ 11 ]. Kapur’s approach used the entropy of the histogram as a formula to obtain the optimal thresholds [ 12 ]. Li et al [ 13 ] presented the minimum cross-entropy to minimize the cross-entropy between the original and segmented image to get the optimal thresholds values.…”