Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Abstract

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

Evaluation Algorithm of Volleyball Players’ Competitive Ability Based on the Random Matrix Model

“…However, for coaches, it is impossible to remember all tactics in order to raise the scientific level of volleyball training. In the process of training and competition, systematic statistics and analysis of techniques and tactics are needed to objectively and effectively record the application of techniques and tactics of athletes [21], so as to understand the characteristics of techniques and tactics of athletes and provide important basis for the development of training plans and competition plans in the next step. In addition, the systematic technical and tactical analysis is of great help to enhance the confidence of athletes and stabilize the mood of competition.…”

An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

“…Literature [16] used the gray wolf algorithm to obtain the best segmentation threshold of the image and obtain higher quality segmentation results. At the same time, literature [17] introduced the whale algorithm to image multithreshold segmentation. Literature [18] proposed image multithreshold segmentation based on harmony algorithm and electromagnetic optimization algorithm.…”

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

“…Further, other fields of Image Processing could be explored using the proposed method 9 Neighborhood Fitness Distance Ratio Teaching Learning based Optimization (NFDR-TLBO) Kapur Entropy Jiang et al ( 2021a ) Standard Gray and Color Images Proposed method is compared with TLBO, FDRPSO, FDR-TLBO, BBPSO, jDE and SaDE PSNR, SSIM and FSIM The proposed NFDR-TLBO algorithm shows exceptional result in most of the cases by suggestively improving TLBO. Though, more research and experiments need to be carried out to determine suitable objective functions to finally regulate optimal threshold values 10 Modified Whale Optimization Algorithm (MWOA) Otsu’s thresholding and Kapur entropy Anitha et al ( 2021 ) Standard Color Images Proposed method is compared with GA, PSO, ABC and CS PSNR, SSIM, FSIM and CPU time The proposed MWOA offers improved performance as compare to other algorithms consuming a reduced amount of computational time 11 Volleyball Premier League using Whale Optimization Algorithm (VPLWOA) Otsu’s thresholding Abd Elaziz et al ( 2021a ) Standard Gray Images Proposed method is compared with SSO, FFA, WOA, VPL and SCA PSNR, SSIM, FSIM and CPU time The sports inspiration based on basic VPL is applied for the very first time in the field of multilevel thresholding. Experimental results confirm that the proposed algorithm outdoes other existing meta-heuristic algorithms 12 Manta Ray Foraging Optimization based on the Opposition-Based Learning (MRFO-OBL) Otsu’s thresholding Houssein et al ( 2021b ) Computed Tomography (CT) Images: Medical Images Proposed method is compared with SCA, MFO, EO, WOA, SSO and MRFO PSNR and SSIM The proposed MRFO-OBL basically finds the finest threshold values to further maximize Otsu’s function.…”