Feature Fusion Methods in Pattern Classification

doi:10.13190/j.jbupt.2017.04.001

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2017, Vol. 40 ›› Issue (4): 1-8.doi: 10.13190/j.jbupt.2017.04.001

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Feature Fusion Methods in Pattern Classification

LIU Wei-bin¹, ZOU Zhi-yuan¹, XING Wei-wei²

1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
2. School of Software Engineer, Beijing Jiaotong University, Beijing 100044, China

Received:2017-03-03 Online:2017-08-28 Published:2017-08-28

Abstract

Abstract: 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.

Key words: feature fusion, pattern recognition, classification

CLC Number:

TP391.4

LIU Wei-bin, ZOU Zhi-yuan, XING Wei-wei. Feature Fusion Methods in Pattern Classification[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(4): 1-8.

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