Multilevel Thresholding for Image Segmentation Using the Galaxy-based Search Algorithm

In this paper, image segmentation of graylevel images is performed by mult ilevel thresholding.The optimal thresholds for this purpose are found by maximizing the between-class variance (the Otsu's criterion).The optimization (maximizat ion) is conducted by a novel nature-inspired search algorith m, which is called Galaxy-based Search Algorith m or Gb SA.The p roposed Gb SA is a metaheuristic for continuous optimizat ion.It resemb les the spiral arms of some galaxies to search for the optimal thresholds.The Gb SA also uses a modified Hill Climbing algorith m as a local search.The GbSA also utilizes chaos for improving its performance, which is imp lemented by the logistic map.Experimental results show that the Gb SA finds the optimal or very near optimal thresholds in all runs of the algorithm.