An Incremental Feature Extraction Method without Estimating Image Covariance Matrix

Xiaofeng WANG; Mingyue SUN; Weimin GE

doi:10.11999/JEIT181138

Volume 41 Issue 11

Nov. 2019

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(11): 2768-2776

Xiaofeng WANG, Mingyue SUN, Weimin GE. An Incremental Feature Extraction Method without Estimating Image Covariance Matrix[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2768-2776. doi: 10.11999/JEIT181138

Citation:

Xiaofeng WANG, Mingyue SUN, Weimin GE. An Incremental Feature Extraction Method without Estimating Image Covariance Matrix[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2768-2776. doi: 10.11999/JEIT181138

Citation:

PDF( 1459 KB)

An Incremental Feature Extraction Method without Estimating Image Covariance Matrix

doi: 10.11999/JEIT181138

Xiaofeng WANG^{1, 2},
Mingyue SUN¹,
Weimin GE^{1, 2
,
,}

1.
Tianjin Key Laboratory for Advanced Mechatronical System Design and Intelligent Control, Tianjin University of Technology, Tianjin 300384, China
2.
National Experimental Teaching Demonstration Center of Electromechanical Engineering, Tianjin University of Technology, Tianjin 300384, China

Funds: The National Key R & D Plan of China (2017YFB1303304), The Tianjin Science and Technology Planed Key Project (17ZXZNGX00110), The Tianjin Natural Science Key Foundation (16JCZDJC30400)

Received Date: 2018-12-10
Rev Recd Date: 2019-05-06

Available Online: 2019-05-22

Publish Date: 2019-11-01

Abstract

Abstract

To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the feature matrices of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. Finally, a series of experiments are carried out with the self-built block dataset, ORL and Yale face datasets, respectively. The results show that the proposed algorithms have significantly improved the performances of the convergence rate, the classification rate and the complexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the average processing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements for incremental learning.
- Pattern recognition,
- Covariance-free,
- Feature extraction,
- Incremental learning,
- Two-Dimensional Principal Component Analysis (2DPCA)

FullText(HTML)

References(20)

References

WANG Hongjuan, HU Jiani, and DENG Weihong. Face feature extraction: a complete review[J]. IEEE Access, 2018, 6: 6001–6039. doi: 10.1109/ACCESS.2017.2784842

CHAUDHARY G, SRIVASTAVA S, and BHARDWAJ S. Feature extraction methods for speaker recognition: A review[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(12): 1–39. doi: 10.1142/S0218001417500410

SOORA N R and DESHPANDE P S. Review of feature extraction techniques for character recognition[J]. IETE Journal of Research, 2018, 64(2): 280–295. doi: 10.1080/03772063.2017.1351323

NEIVA D H and ZANCHETTIN C. Gesture recognition: A review focusing on sign language in a mobile context[J]. Expert Systems with Applications, 2018, 103: 159–183. doi: 10.1016/j.eswa.2018.01.051

陈小龙, 关键, 于晓涵, 等. 基于短时稀疏时频分布的雷达目标微动特征提取及检测方法[J]. 电子与信息学报, 2017, 39(5): 1017–1023. doi: 10.11999/JEIT161040

CHEN Xiaolong, GUAN Jian, YU Xiaohan, et al. Radar micro-Doppler signature extraction and detection via short-time sparse time-frequency distribution[J]. Journal of Electronics &Information Technology, 2017, 39(5): 1017–1023. doi: 10.11999/JEIT161040

ISLAM S, ANAND S, HAMID J, et al. Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration[J]. Statistical Applications in Genetics and Molecular Biology, 2017, 16(3): 199–216. doi: 10.1515/sagmb-2016-0066

BRO R and SMILDE A K. Principal component analysis[J]. Analytical Methods, 2014, 6(9): 2812–2831. doi: 10.1039/c3ay41907j

YANG Jian, ZHANG David, FRANGI A F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131–137. doi: 10.1109/TPAMI.2004.1261097

ZHANG Daoqiang and ZHOU Zhihua. (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition[J]. Neurocomputing, 2005, 69(1-3): 224–231. doi: 10.1016/j.neucom.2005.06.004

ZUO Wangmeng, ZHANG David, and WANG Kuanquan. Bidirectional PCA with assembled matrix distance metric for image recognition[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , 2006, 36(4): 863–872. doi: 10.1109/TSMCB.2006.872274

YANG Wankou, SUN Changyin, and RICANEK K. Sequential Row–Column 2DPCA for face recognition[J]. Neural Computing and Applications, 2012, 21(7): 1729–1735. doi: 10.1007/s00521-011-0676-5

GAO Quanxue, MA Lan, LIU Yang, et al. Angle 2DPCA: A new formulation for 2DPCA[J]. IEEE Transactions on Cybernetics, 2018, 48(5): 1672–1678. doi: 10.1109/TCYB.2017.2712740.

ZHOU Shuisheng and ZHANG Danqing. Bilateral angle 2DPCA for face recognition[J]. IEEE Signal Processing Letters, 2019, 26(2): 317–321. doi: 10.1109/LSP.2018.2889925

DIAZ-CHITO K, FERRI F J, and HERNÁNDEZ-SABATÉ A. An overview of incremental feature extraction methods based on linear subspaces[J]. Knowledge-Based Systems, 2018, 145: 219–235. doi: 10.1016/j.knosys.2018.01.020

WENG Juyang, ZHANG Yilu, and HWANG Weyshiuan. Candid covariance-free incremental principal component analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2003, 25(8): 1034–1040. doi: 10.1109/TPAMI.2003.1217609

王肖锋, 张明路, 刘军. 基于增量式双向主成分分析的机器人感知学习方法研究[J]. 电子与信息学报, 2018, 40(3): 618–625. doi: 10.11999/JEIT170561

WANG Xiaofeng, ZHANG Minglu, and LIU Jun. Robot perceptual learning method based on incremental bidirectional principal component analysis[J]. Journal of Electronics &Information Technology, 2018, 40(3): 618–625. doi: 10.11999/JEIT170561

谢自强, 葛为民, 王肖锋, 等. 发展型机器人实时特征提取方法研究[J]. 机器人, 2017, 39(2): 189–196. doi: 10.13973/j.cnki.robot.2017.0189

XIE Ziqiang, GE Weimin, WANG Xiaofeng, et al. Real time feature extraction method of developmental robot[J]. Robot, 2017, 39(2): 189–196. doi: 10.13973/j.cnki.robot.2017.0189

REN Chuanxian and DAI Daoqing. Incremental learning of bidirectional principal components for face recognition[J]. Pattern Recognition, 2010, 43(1): 318–330. doi: 10.1016/j.patcog.2009.05.020

曹向海, 刘宏伟, 吴顺君. 一种有效的增量BDPCA算法[J]. 系统仿真学报, 2008, 20(20): 5530–5533. doi: 10.16182/j.cnki.joss.2008.20.041

CAO Xianghai, LIU Hongwei, and WU Shunjun. A kind of efficient incremental BDPCA algorithm[J]. Journal of System Simulation, 2008, 20(20): 5530–5533. doi: 10.16182/j.cnki.joss.2008.20.041

文颖, 施鹏飞. 一种基于共同向量结合2DPCA的人脸识别方法[J]. 自动化学报, 2009, 35(2): 202–205. doi: 10.3724/SP.J.1004.2009.00202

WEN Ying and SHI Pengfei. An approach to face recognition based on common vector and 2DPCA[J]. Acta Automatica Sinica, 2009, 35(2): 202–205. doi: 10.3724/SP.J.1004.2009.00202

Relative Articles

Supplements(0)

Cited By

Proportional views