Study of improved immune genetic algorithm for threshold image segmentation based on fuzzy maximum entropy

In the paper a novel improved immune genetic algorithm is proposed for thresholding image segmentation based on the maximum entropy. At first, the encoded mode is made and the maximum entropy function is selected as the key adaptation genetic algorithm. Then, with the method of regulating density in the immune arithmetic, the ICM algorithm is adopted and the better antibody is transfered to the next generation. And more, the parameter of cross operator and mutation operator are mended appropriately. In the end, Comparing with the standard genetic algorithm, the improved immune genetic algorithm can enhance efficiency of running, form the results of the experiment we can see that the improved algorithm has also some advantages, such as validity and practicability.