Otsu´s criterion-based multilevel thresholding by a nature-inspired metaheuristic called Galaxy-based Search Algorithm

In this paper, image segmentation of gray-level images is performed by multilevel thresholding. The optimal thresholds for this purpose are found by maximizing the between-class variance (the Otsu´s criterion). The optimization is conducted by a newly-developed nature-inspired metaheuristic called “Galaxy-based Search Algorithm” or the GbSA. The proposed GbSA resembles the spiral arms of some galaxies to search for the optimal thresholds. The GbSA also uses a modified Hill Climbing algorithm as a local search. The experimental results show that the GbSA finds the optimal or very near optimal thresholds in all runs of the algorithm.