Quantum inspired meta-heuristic approaches for automatic clustering of colour images

Abstract

In this article, quantum inspired incarnations of two swarm based meta-heuristic algorithms, namely, Crow Search Optimization Algorithm and Intelligent Crow Search Optimization Algorithm have been proposed for automatic clustering of colour images. The performance and effectiveness of the proposed algorithms have been judged by experimenting on 15 Berkeley images and five publicly available real life images of different sizes. The validity of the proposed algorithms has been justified with the help of four different cluster validity indices, namely, Pakhira Bandyopadhyay Maulik, I-index, Silhouette and CS-measure. Moreover, Sobol's sensitivity analysis has been performed to tune the parameters of the proposed algorithms. The experimental results prove the superiority of proposed algorithms with respect to optimal fitness, computational time, convergence rate, accuracy, robustness, t-test and Friedman test. Finally, the efficacy of the proposed algorithms has been proved with the help of quantitative evaluation of segmentation evaluation metrics.