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 without 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 the advanced nature of the method.