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.
image segmentation maximum entropy immune genetic algorithmy
Jiang Hua wei Yang kai
College of Information Science and Engineering Henan University of Technology Zhengzhou, China
国际会议
长春
英文
52-55
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）