针对目标与背景两类图像模式识别问题,在已有的特征选择方法基础上,提出了一种新颖的基于免疫分子编码机理的图像特征选择方法(IACA). 该方法借鉴生物免疫系统的抗体分

子编码机理,在对样本进行参数估计情况下,提出熵度量单个特征对于目标和背景的识别敏感度 从集合的角度研究并且定义了特征之间的包含和互补关系 并且基于组成抗体分子氨基酸结合能量最小原则,提出了关于图像目标的免疫抗体构建规则 最终实现了寻找最优特征子集的算法IACA ,该特征子集的维数通过算法自动获得无需人为设定,选择结果为目标的“免疫抗体”,能很好的从背景中识别目标. 利用归纳法证明了用IACA 得到的特征子集的最优性. 与其他特征选择方法比较,测试结果显示该算法具有较低的计算复杂度和错误识别率,表明了该方法的优越性和先进性.-Aiming at two classes image pattern recognition problem of object and background , a novel image feature selection method ,named immune antibody construction algorithm ( IACA) is proposed , inspired by the biological immune antibody encoding principle. In the case of sample parameter estimation , IACA considers entropy to measure individual feature’s sensitivity of object and background ,and defines the inclusion and complementary formulas about multi features in set theory perspective. Guided by the minimum energy principle , image immune antibody construction rules and corresponding algorithm are proposed to find an

optimized feature subset as object immune antibody. Furthermore ,the dimension of the subset can be automatically determined with out prior setting. The induction proved the result was the optimal feature subset. Data testing result shows that IACA has a lower computational complexity and error recognition rate than other methods ,which has verified the superiority and t