A Joint Entropy for Image Segmentation Based on Quasi Opposite Multiverse Optimization

Image segmentation is the initial task in image processing which is extensively utilized in object recognition and detection. In the field of image segmentation, multilevel thresholding is one of the leading methods. Though, the computational cost of this method scales exponentially as the number of the threshold value increases, which directs to exercise of optimization method to determine the optimal value of the thresholds. In this article, a newly modified algorithm called quasi opposite multiverse optimization (QOMVO) is proposed. The proposed QOMVO is based on quasi opposite based learning and multiverse optimization (MVO) algorithm. The quasi opposite based learning helps to improve the exploration phase of QOMVO. QOMVO is coupled with a new proposed entropy called Joint entropy (Renyi-Tsalii) to perform image segmentation by finding the optimal threshold value. The outcome of the proposed algorithm is compared with other evolutionary algorithm based on objective function value, feature similarity index, structural similarity index , quality index based on local variance, uniformity, normalized absolute error and computational time. A non-parametric test called the Wilcoxon test is done to justify the response of these parameters. Along with comparing with other algorithms, a comparison has also been made with other entropy, i.e. Renyi’s and Tsallis. The experimental outcomes confirmed that the proposed algorithm provides more reliable results than other existing methods.