基于Fisher准则的多特征融合

Multifeature Fusion Based on Fisher Discriminant Criterion

阐述了单个特征向量及其鉴别矢量与模式可分性的关系最佳鉴别矢量使模式关于该特征具有最大的可分性。给出了多特征融合的一,种方法,它综合考查了模式对不同的特征、不同的鉴别矢量的可分性,由多个特征经融合产生的新特征吸收了单个特征的对模式分类的优势。手写体汉字的识别试验验证了所给方法的有效性。

The relationship between single feature vector together with its discriminant vector and the separability the pattern can get is discussed in this paper, from that we know optimal discriminant vector can result in the best separability. We propose a new method of multi-features fusion in this paper, it considers all discriminant performances made by different features and different discriminant vectors, and the new feature produced by multi-features fusion has the advantages hold by every single feature. The experiments of handwritten Chinese character recognition show the effectiveness of the proposed approach.