基于谱回归特征降维与后向传播神经网络的识别方法研究

doi:10.11999/JEIT150781

Abstract
Figure/Table
References (18)
Related Citation (15)

Download: PDF (2249 KB)
Export: BibTeX | EndNote (RIS)

Abstract

When using Back Propagation (BP) neural network to recognize the spatial target, the high dimensional input features induce the complexity of the network structure and the poor performance of the recognition. In this paper, a new recognition method based on Spectral Regression (SR) feature dimension reduction and BP neural network is proposed for the above difficulties. Firstly, the HOG features are extracted from the spatial object, and then the feature dimensions are reduced by SR. Finally, the BP classifier is used to train the data. Experimental results show that the proposed method is better than the traditional dimension reduction methods such as PCA, KPCA, LPP, KLPP in dimension reduction and recognition, which can juggle real-time and accuracy, thus improving the recognition performance.

Key words： Target recognition Back Propagation (BP) neural network Spectral Regression (SR) Feature dimension reduction

Received: 29 June 2015 Published: 18 December 2015

PACS:

TP391.4

Corresponding Authors: NIU Min E-mail: niuminbuaa@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WU Zhanjun
	NIU Min
	XU Bing
	NIU Yanxiong
	GENG Tianqi
	ZHANG Fan
	MAN Da

Cite this article:

WU Zhanjun,NIU Min,XU Bing等. Research on Recognition Method Based on Spectral Regression and Back Propagation Neural Network[J]. JEIT, 2016, 38(4): 978-984.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150781 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I4/978

[1]	乔俊飞, 李淼, 刘江. 一种神经网络快速修剪算法[J]. 电子学报, 2010, 38(4): 830-834.
	QIAO Junfei, LI Miao, and LIU Jiang. A fast pruning algorithm for neural network[J]. Acta Electronica Sinca, 2010, 38(4): 830-834.
[2]	曾岳, 冯大政. 一种基于加权变形的2DPCA的人脸特征提取方法[J]. 电子与信息学报, 2011, 33(4): 769-774. doi: 10.3724/ SP.J.1146.2010.01003.
	ZENG Yue and FENG Dazheng. An algorithm of feature extraction of face based on the weighted variation of 2DPC[J]. Journal of Electronics & Information Technology, 2011, 33(4): 769-774. doi: 10.3724/SP.J.1146.2010.01003.
[3]	马原, 吕群波, 刘扬阳. 基于主成分变换的图像稀疏度估计方法[J]. 物理学报, 2013, 62(20): 204202-1-204202-11. doi: 10.7498/aps.62.204202.
	MA Yuan, Lü Qunbo, and LIU Yangyang. Image sparsity evaluation based on principle component analysis[J]. Acta Physica Sinica, 2013, 62(20): 204202-1-204202-11. doi: 10.7498/aps.62.204202.
[4]	孙韶媛, 李琳娜, 赵海涛. 采用KPCA和BP神经网络的单目车载红外图像深度估计[J]. 红外与激光工程, 2013, 42(9): 2348-2352.
	SUN Shaoyuan, LI Linna, and ZHAO Haitao. Depth estimation from monocular vehicle infrared images based on KPCA and BP neural network[J]. Infrared and Laser Engineering, 2013, 42(9): 2348-2352.
[5]	ZHENG W, LAI J, and YUEN P C. Penalized preimage learning in kernel principal component analysis[J]. IEEE Transactions on Neural Networks, 2010, 21(4): 551-570.
[6]	ZHANG L M, QIAO L S, and CHEN S C. Graph-optimized locality preserving projections[J]. Pattern Recognition, 2010, 43(6): 1993-2002.
[7]	张志伟, 杨帆, 夏克文, 等. 一种有监督的LPP算法及其在人脸识别中的应用[J]. 电子与信息学报, 2008, 30(3): 539-541.
	ZHANG Zhiwei, YANG Fan, XIA Kewen, et al. A supervised LPP algorithm and its application to face recognition[J]. Journal of Electronics & Information Technology, 2008, 30(3): 539-541.
[8]	于攀, 叶俊勇. 基于谱回归和核空间最近邻的基因表达数据分类[J]. 电子学报, 2011, 39(8): 1955-1960.
	YU Pan and YE Junyong. Spectral regression and kernel space K-nearest neighbor for classification of gene expression data[J]. Acta Electronica Sinica, 2011, 39(8): 1955-1960.
[9]	NARNEET D and BILL T. Histograms of oriented gradients for human detection[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005: 886-893.
[10]	SU X, LIN W, ZHENG X, et al. A new local-main gradient orientation HOG and contour differences based algorithm for object classification[C]. IEEE International Symposium on Circuits and Systems, Beijing, 2013: 2892-2895.
[11]	罗会兰, 钟宝康, 孔繁胜. 带权分块压缩感知的预测目标跟踪算法[J]. 电子与信息学报, 2015, 37(5): 1160-1166. doi: 10.11999/JEIT140997.
	LUO Huilan, ZHONG Baokang, and KONG Fansheng. Tracking using weighted block compressed sensing and location prediction[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1160-1166. doi: 10.11999/ JEIT140997.
[12]	韩贵金, 朱虹. 基于HOG和颜色特征融合的人体姿态估计[J]. 模式识别与人工智能, 2014, 27(9): 769-777.
	HAN Guijin and ZHU Hong. Human pose estimation based on fusion of HOG and color feature[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(9): 769-777.
[13]	宋丹, 唐林波, 赵保军. 基于仿射梯度方向直方图特征的目标识别算法[J]. 电子与信息学报, 2013, 35(6): 1428-1434. doi: 10.3724/SP.J.1146.2012.01241.
	SONG Dan, TANG Linbo, and ZHAO Baojun. The object recognition algorithm based on affine histogram of oriented gradient[J]. Journal of Electronics & Information Technology, 2013, 35(6): 1428-1434. doi: 10.3724/SP.J.1146.2012.01241.
[14]	姜伟, 张晶, 扬炳儒. 自适应正则化核二维判别分析[J]. 模式识别与人工智能, 2014, 27(12): 1089-1097.
	JIANG Wei, ZHANG Jing, and YANG Bingru. Adaptive regularization based kernel two dimensional discriminant analysis[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(12): 1089-1097.
[15]	孙丽娟. 基于谱回归的人脸识别的研究[D]. [硕士论文], 重庆大学, 2009.
	SUN Lijuan. Research on the face recognition based on spectral regression[D]. [Master dissertation], Chongqing University, 2009.
[16]	潘炜深, 金连文, 冯子勇. 基于多尺度梯度及深度神经网络的汉字识别[J]. 北京航空航天大学学报, 2015, 41(4): 751-756. doi: 10.13700/j.bh. 1001-5965.2014.0499.
	PAN W S, JIN L W, and FENG Z Y. Recognition of Chinese characters based on multi-scale gradient and deep neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(4): 751-756. doi: 10.13700/j.bh. 1001-5965.2014.0499.
[17]	张海燕, 李欣, 田书峰. 基于BP神经网络的仿真线设计及其FPGA实现[J]. 电子与信息学报, 2007, 29(5): 1262-1265.
	ZHANG Haiyan, LI Xin, and TIAN Shufeng. Simulation line design and its FPGA realization based on BP neural network[J]. Journal of Electronics & Information Technology, 2007, 29(5): 1262-1265.
[18]	孙琼, 王光飞. 基于BP神经网络与HMM的驾驶状态识别[C]. 2014中国汽车工程学会年会, 上海, 2014: 430-432.
	SUN Qiong and WANG Guangfei. Driving state recognition based on BP neural network and HMM[C]. 2014 SAE-China Congress & Exhibition, Shanghai, 2014: 430-432.