%0 Journal Article
%A LIU Wei-bin
%A XING Wei-wei
%A ZOU Zhi-yuan
%T Feature Fusion Methods in Pattern Classification
%D 2017
%R 10.13190/j.jbupt.2017.04.001
%J Journal of Beijing University of Posts and Telecommunications
%P 1-8
%V 40
%N 4
%X Feature fusion is an important method in pattern recognition. Image recognition problem in computer vision can be known as a special pattern classification problem, and it still have many challenges. To solve the problem, feature fusion method is able to use multi-feature of image, complement each other's advantages and get more robust and accurate results. Based on information fusion theory, analyze the theory of feature fusion and introduce the development of feature fusion methods. Besides, discuss three methods of feature fusion combining with other basic theories. Bayesian decision theory based feature fusion algorithm implements fusion decision of multi-feature. Sparse representation based feature fusion algorithm can get the joint sparse representation of multi-feature. Deep learning based feature fusion algorithm intensifies feature learning process of deep neural network model.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017.04.001