An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation.

Shubham Mahajan,Amit Kant Pandit,Mehedi Masud,Rohit Salgotra,Nitin Mittal,Hesham A Alhumyani,Huiling Chen

doi:10.1155/2022/2794326

Abstract

Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets.

Highlights

Nature-inspired methods are applied in most engineering research problems because of their linear nature, easy implementation, and randomization dependent on population
NP is the population size, D is the dimension of problem, Gmax is the number of iteration, Nmax is the maximum number of runners, σ is the standard deviation, bp is the breeders’ probability, and PR are different threshold levels
The results show that TII-FE adaptive salp swarm algorithm (ASSA) is an effective image thresholding approach

Summary

Introduction

Nature-inspired methods are applied in most engineering research problems because of their linear nature, easy implementation, and randomization dependent on population. Multilevel picture thresholding was used for the gray wolf optimization process (GWO); an objective function was dependent on Otsu’s class variance method [22] and Kapur’s entropy. A novel technique of ASSA along with thresholding methods is proposed for image segmentation, which is an area of research with high accuracy in segmentation It is practically validated by testing the accuracy of outputs and computational time taken by many other existing, state-of-the-art algorithms like GA [2], PSO [8], FPA [5], BA [6, 7], CS [9], DE [1], and MPA [10].

Thresholding in Multilevel Images

Adaptive Salp Swarm Algorithm

Result and Discussion

Conclusion and Future Scope