基于克隆选择的图像分割算法研究  

Image Segmentation Algorithm Based on Clone Selection

作　　者：金章赞[1] 肖刚[1] 陈久军[1] 高飞[1] 周鸿斌[1] 

机构地区：[1]浙江工业大学信息工程学院,杭州310032

出　　处：《中国图象图形学报》2010年第2期255-260,共6页Journal of Image and Graphics

基　　金：国家自然科学基金资助项目(50705087);浙江省自然科学基金资助项目(Y106602)

摘　　要：为了快速有效地对生物运动图像进行分割,提出了一种新的图像分割算法,该方法基于传统的2维最大熵分割法,将计算机免疫学中的克隆选择算法应用于图像分割中。首先对图像2维阈值进行编码,然后依据2维最大熵准则建立亲和力函数,在给定的初始种群下,对种群进行选择、克隆、变异、更新等操作,由于克隆选择算法具有生物免疫系统自组织、自学习、自识别、自记忆的能力,所以能够快速地得到全局最优解,实现图像的有效分割。从实验结果表明该算法所用搜索时间大约只有标准2维最大熵法的1.3%,证明该算法高效稳定,是一种实用有效的图像分割算法。In order to effectively segment biological movement image, this paper inspired by the two-dimension maximum entropy, use the clone selection algorithm of computer immunology into the image segmentation. First, this method codes the two-dimension maximum entropy. Second, the fitness function is established according to the criterion function of the two-dimension maximum entropy. Then with the given initial population, we execute selection, cloning, mutation and updating to the population, finally get the best result which can segment image efficiently. The experimental results indicate that this algorithm spend the search time approximately only then the ordinary two-dimensional biggest entropy method 1.3% . This proof the method is feasible and effective.

关 键 词：图像分割 2维最大熵 克隆选择 免疫算法 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]