关键词: 边界总和最大化, 特征融合, Pseudo-Zernike矩, Gabor特征, 椭圆基概率神经网络

Abstract: A novel fusion algorithm called Marginal Summation Maximum Based Feature Fusion（MSMFF） is proposed on license plate Chinese character classification.On the basis of the normal distribution nature of high dimension data projected to low dimension space，a tandem high dimension feature consisting of Pseudo-Zernike Invariant Moments（PZIM） and Gabor Coefficients（GC） is projected to low dimension space and a marginal summation is constructed by class mean vectors and variance vectors.And this kind of margin is maximized，the optimal projection matrix is obtained and thus a new fused feature is gained as the input of Elliptic Basis Probabilistic Neural Network（EBPNN）.Experiments show that new fused feature makes the best of the macrocosmic characterization ability of PZIM and the local depiction power of GC and effectively compress data capacity simultaneously.So the proposed method enhances the rate of classification as well as decreases the algorithm complexity，and EBPNN is also a powerful classifier compared with traditional one such as SVM.

Key words: marginal summation maximum, feature fusion, Pseudo-Zernike Invariant Moments（PZIM）, Gabor feature, Elliptic Basis Probabilistic Neural Network（EBPNN）