TY - GEN
T1 - Object recognition with multi-source images based on kernel dictionary learning
AU - Jun, Xu
AU - Li, Yuanxiang
AU - Xian, Wei
AU - Peng, Xishuai
AU - Lu, Yongshuai
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - With the development of widely-used unmanned aerial vehicles (UAV), automatic object recognition for UAV aerial images has important practical values. Since the background of objects is complex, there are limitations in object recognition using single-source visible or infrared data. Multi-source images contain much more information of objects, which can improve the recognition rate. Meanwhile there exist the problems of high dimension and nonlinear separability between features. In order to solve these problems, a recognition algorithm based on kernel dictionary learning is proposed. First, the algorithm learns a kernel dictionary and then obtains the sparse representations of objects by the kernel dictionary. Then the linear discriminant analysis is used to discriminate the sparse representations. Finally, the support vector machine is employed to classify four kinds of objects. The experimental results on visible and infrared images show that our method based on kernel dictionary learning has superior recognition performance in comparison with the methods based on traditional feature extraction and dictionary learning.
AB - With the development of widely-used unmanned aerial vehicles (UAV), automatic object recognition for UAV aerial images has important practical values. Since the background of objects is complex, there are limitations in object recognition using single-source visible or infrared data. Multi-source images contain much more information of objects, which can improve the recognition rate. Meanwhile there exist the problems of high dimension and nonlinear separability between features. In order to solve these problems, a recognition algorithm based on kernel dictionary learning is proposed. First, the algorithm learns a kernel dictionary and then obtains the sparse representations of objects by the kernel dictionary. Then the linear discriminant analysis is used to discriminate the sparse representations. Finally, the support vector machine is employed to classify four kinds of objects. The experimental results on visible and infrared images show that our method based on kernel dictionary learning has superior recognition performance in comparison with the methods based on traditional feature extraction and dictionary learning.
KW - Automatic object recognition
KW - kernel dictionary learning
KW - linear discriminant analysis
KW - multi-source images
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85016271551
U2 - 10.1109/ICSP.2016.7877999
DO - 10.1109/ICSP.2016.7877999
M3 - 会议稿件
AN - SCOPUS:85016271551
T3 - International Conference on Signal Processing Proceedings, ICSP
SP - 1100
EP - 1105
BT - ICSP 2016 - 2016 IEEE 13th International Conference on Signal Processing, Proceedings
A2 - Baozong, Yuan
A2 - Qiuqi, Ruan
A2 - Yao, Zhao
A2 - Gaoyun, An
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Signal Processing, ICSP 2016
Y2 - 6 November 2016 through 10 November 2016
ER -