模式分类中的特征融合方法

doi:10.13190/j.jbupt.2017.04.001

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (4): 1-8.doi: 10.13190/j.jbupt.2017.04.001

• 综述 • 下一篇

模式分类中的特征融合方法

刘渭滨¹, 邹智元¹, 邢薇薇²

1. 北京交通大学信息科学研究所, 北京 100044;
2. 北京交通大学软件学院, 北京 100044

收稿日期:2017-03-03 出版日期:2017-08-28 发布日期:2017-08-28
作者简介:刘渭滨(1973-),男,教授,博士生导师;邹智元(1991-),男,博士生,E-mail:zouzhiyuan@bjtu.edu.cn.
基金资助:
国家自然科学基金项目（61370127，61473031，61472030）；新世纪优秀人才支持计划项目（NCET-13-0659）；中央高校基本科研业务费专项资金项目（2014JBZ004）；北京高等学校青年英才计划项目（YETP0583）

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

摘要/Abstract

摘要： 特征融合方法是模式识别领域的一种重要方法.计算机视觉领域的图像识别问题作为一种特殊的模式分类问题，仍然存在很多挑战.特征融合方法能够综合利用多种图像特征，实现多特征的优势互补，获得更加鲁棒和准确的识别结果.笔者基于信息融合理论分析了特征融合方法的原理，介绍了特征融合方法的研究现状，讨论了特征融合与3类主流基础理论相结合的方法，其中基于贝叶斯理论的特征融合算法可以实现多特征的融合决策，基于稀疏表示理论的特征融合算法能够得到多特征的联合稀疏表示，基于深度学习理论的特征融合算法能够强化深度神经网络模型的特征学习过程.

关键词: 特征融合, 模式识别, 分类

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

中图分类号:

TP391.4

刘渭滨, 邹智元, 邢薇薇. 模式分类中的特征融合方法[J]. 北京邮电大学学报, 2017, 40(4): 1-8.

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.

参考文献

[1] Sanderson C, Paliwal K K. Identity verification using speech and face information[J]. Digital Signal Processing, 2004, 14(5):449-480.
[2] Kittler J, Hatef M, Duin R P W, et al. On combining classifiers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3):226-239.
[3] 韩崇昭, 朱洪艳, 段战胜等. 多源信息融合[M]. 北京:清华大学出版社, 2010.
[4] Kuncheva L I. A theoretical study on six classifier fusion strategies[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(2):281-286.
[5] Jain A, Nandakumar K, Ross A. Score normalization in multimodal biometric systems[J]. Pattern Recognition, 2005, 38(12):2270-2285.
[6] Nandakumar K, Chen Y, Dass S C, et al. Likelihood ratio-based biometric score fusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 30(2):342.
[7] Ma A J, Yuen P C, Lai J H. Linear dependency modeling for classifier fusion and feature combination[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5):1135-1148.
[8] Shekhar S, Patel V M, Nasrabadi N M, et al. Joint sparse representation for robust multimodal biometrics recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1):113-126.
[9] Yan J, Zheng W, Xu Q, et al. Sparse kernel reduced-rank regression for bimodal emotion recognition from facial expression and speech[J]. IEEE Transactions on Multimedia, 2016, 18(7):1319-1329.
[10] Bai S, Bai X. Sparse contextual activation for efficient visual re-ranking[J]. IEEE Transactions on Image Processing, 2016, 25(3):1056-1069.
[11] Zhang L, Zhang D. Visual understanding via multi-feature shared learning with global consistency[J]. IEEE Transactions on Multimedia, 2016, 18(2):247-259.
[12] Yu M, Liu L, Shao L. Structure-preserving binary representations for RGB-D action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(8):1651-1664.
[13] Demirkus M, Precup D, Clark J, et al. Hierarchical spatio-temporal probabilistic graphical model with multiple feature fusion for binary facial attribute classification in real-world face videos[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(6):1185-1203.
[14] Hu J F, Zheng W S, Lai J, et al. Jointly learning heterogeneous features for RGB-D activity recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016(99):1-1.
[15] Feichtenhofer C, Pinz A, Zisserman A. Convolutional two-stream network fusion for video action recognition[C]//CVPR 2016. Las Vegas:IEEE Computer Society, 2016:1933-1941.
[16] Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos[J]. Advances in Neural Information Processing Systems, 2014, 1(4):568-576.
[17] Zhang X, Zhang H, Zhang Y, et al. Deep fusion of multiple semantic cues for complex event recognition[J]. IEEE Transactions on Image Processing, 2016, 25(3):1033-1046.

模式分类中的特征融合方法

Feature Fusion Methods in Pattern Classification

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