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基于圖像協(xié)方差無關(guān)的增量特征提取方法研究

王肖鋒 孫明月 葛為民

王肖鋒, 孫明月, 葛為民. 基于圖像協(xié)方差無關(guān)的增量特征提取方法研究[J]. 電子與信息學報, 2019, 41(11): 2768-2776. doi: 10.11999/JEIT181138
引用本文: 王肖鋒, 孫明月, 葛為民. 基于圖像協(xié)方差無關(guān)的增量特征提取方法研究[J]. 電子與信息學報, 2019, 41(11): 2768-2776. doi: 10.11999/JEIT181138
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

基于圖像協(xié)方差無關(guān)的增量特征提取方法研究

doi: 10.11999/JEIT181138
基金項目: 國家重點研發(fā)計劃(2017YFB1303304),天津市科技計劃重大專項(17ZXZNGX00110),天津市自然科學重點基金(16JCZDJC30400)
詳細信息
    作者簡介:

    王肖鋒:男,1977年生,博士,講師,研究方向為發(fā)育機器人和機器學習

    孫明月:女,1994年生,碩士生,研究方向為機器人智能學習

    葛為民:男,1968年生,博士,教授,研究方向為機器人智能控制

    通訊作者:

    葛為民 geweimin@tjut.edu.cn

  • 中圖分類號: TP391.41

An Incremental Feature Extraction Method without Estimating Image Covariance Matrix

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)
  • 摘要: 針對2維主成分分析(2DPCA)算法無法實現(xiàn)在線特征提取及無法體現(xiàn)完整數(shù)據(jù)結(jié)構(gòu)信息等問題,該文提出一種基于圖像協(xié)方差無關(guān)的增量式2DPCA(I2DPCA)算法。該算法無需對圖像協(xié)方差矩陣進行特征值分解奇異值分解,復雜度將大為降低,提高了特征提取速度。針對I2DPCA僅提取了橫向特征的問題,又提出一種增量式行列順序2DPCA(IRC2DPCA)算法,該算法對I2DPCA的特征矩陣再次進行縱向特征提取,保留了圖像的橫向與縱向結(jié)構(gòu)信息,實現(xiàn)了行列兩個方向上的特征提取與數(shù)據(jù)降維。最后,以自建的物塊數(shù)據(jù)集、通用的ORL和Yale人臉數(shù)據(jù)集分別進行對比實驗,結(jié)果表明,該文算法在收斂率、分類率及復雜度等性能方面均得到了顯著提高,其收斂率達到99%以上,分類率可達97.6%,平均處理速度為29 幀/s,能夠滿足增量特征提取的實時處理需求。
  • 圖  1  部分物塊樣本圖像

    圖  2  部分ORL人臉樣本圖像

    圖  3  部分Yale人臉樣本圖像

    圖  4  物塊數(shù)據(jù)集循環(huán)多次特征向量的收斂率

    表  1  不同數(shù)據(jù)集下特征向量收斂率的均值及標準差

    數(shù)據(jù)集輸入次數(shù)I2DPCA(均值/標準差)IRC2DPCA(均值/標準差)
    前4個特征向量后4個特征向量前4個特征向量后4個特征向量
    物塊$m = 2$0.99442/0.003610.86836/0.178260.98964/0.005730.96564/0.01135
    $m = 5$0.99924/0.000410.95478/0.158180.99824/0.001010.97302/0.01020
    $m = 10$0.99981/0.000100.98453/0.022250.99950/0.000300.99495/0.00322
    ORL$m = 2$0.99991/0.000040.89400/0.067630.99753/0.002030.98175/0.01027
    $m = 5$0.99997/0.000020.91620/0.051540.99951/0.000370.99441/0.00231
    $m = 10$0.99998/0.000010.92833/0.051630.99986/0.000100.99755/0.00074
    Yale$m = 2$0.93732/0.072440.96069/0.030260.99602/0.004410.98308/0.00659
    $m = 5$0.96001/0.046780.98261/0.016090.99924/0.000740.99528/0.00140
    $m = 10$0.97283/0.031800.99041/0.009700.99975/0.000240.99793/0.00088
    下載: 導出CSV

    表  2  物塊數(shù)據(jù)集的最佳分類率

    每類訓練
    樣本數(shù)
    2DPCA
    (120×4)[8]
    RC2DPCA
    (8×8)[11]
    Angle-2DPCA
    (120×4)[12]
    BA2DPCA
    (8×8)[13]
    BDPCA
    (8×8)[10]
    IBDPCA
    (8×8)[17]
    I2DPCA
    (120×4)
    IRC2DPCA
    (8×8)
    L=100.9330.9270.9220.9310.9300.9300.9260.922
    L=250.9570.9600.9580.9610.9590.9590.9620.968
    L=500.9590.9610.9610.9610.9610.9670.9620.972
    L=750.9540.9570.9540.9570.9580.9560.9550.958
    L=1000.9660.9630.9660.9640.9630.9730.9710.976
    下載: 導出CSV

    表  3  ORL數(shù)據(jù)集的最佳分類率

    每類訓練
    樣本數(shù)
    2DPCA
    (112×4)[8]
    RC2DPCA
    (8×8)[11]
    Angle-2DPCA
    (112×4)[12]
    BA2DPCA
    (8×8)[13]
    BDPCA
    (8×8)[10]
    IBDPCA
    (8×8)[17]
    I2DPCA
    (112×4)
    IRC2DPCA
    (8×8)
    L=10.7440.7280.7420.7250.7280.7080.7360.717
    L=20.8500.8440.8500.8470.8470.8440.8470.841
    L=30.8680.8570.8680.8610.8610.8460.8640.857
    L=40.8880.8960.8880.8920.9040.9040.8830.900
    L=50.9050.9050.9050.9050.9150.9150.9050.925
    下載: 導出CSV

    表  4  Yale數(shù)據(jù)集的最佳分類率

    每類訓練
    樣本數(shù)
    2DPCA
    (100×4)[8]
    RC2DPCA
    (8×8)[11]
    Angle-2DPCA
    (100×4)[12]
    BA2DPCA
    (8×8)[13]
    BDPCA
    (8×8)[10]
    IBDPCA
    (8×8)[17]
    I2DPCA
    (100×4)
    IRC2DPCA
    (8×8)
    L=10.5600.5600.5600.5600.5600.5530.5730.560
    L=20.7190.7330.7190.7330.7410.7410.7260.726
    L=30.8000.8080.7920.8000.8170.8250.7920.825
    L=40.8570.8760.8570.8760.8760.8760.8570.867
    L=50.8560.8890.8560.8890.8890.8890.8560.889
    下載: 導出CSV

    表  5  物塊數(shù)據(jù)集處理的所需時間對比(s)

    算法L=10L=25L=50L=75L=100
    特征提取分類識別特征提取分類識別特征提取分類識別特征提取分類識別特征提取分類識別
    2DPCA(120×4)[8]2.1525.2799.5686.56132.7457.99069.0659.015123.77910.319
    RC2DPCA(8×8)[11]2.0290.9419.2081.14032.6301.36266.4461.335115.3441.554
    Angle-2DPCA(120×4)[12]4.9911.09715.6041.17493.281.29667.7881.47157.6241.545
    BA2DPCA(8×8)[13]13.1171.17914.2611.19046.1071.20365.1051.25048.0891.233
    BDPCA(8×8)[10]2.3560.9339.4560.96632.9881.24867.5011.414127.391.556
    IBDPCA(8×8)[17]11.8260.86829.0741.05556.7781.22186.1201.408114.8451.604
    I2DPCA(120×4)3.1035.3617.3816.22514.6937.77821.9388.96329.03911.181
    IRC2DPCA(8×8)7.1590.84617.0471.04334.4131.26651.5771.44365.5101.669
    下載: 導出CSV

    表  6  物塊數(shù)據(jù)集處理的所需內(nèi)存對比(kB)

    算法L=10L=25L=50L=75L=100
    特征提取分類識別特征提取分類識別特征提取分類識別特征提取分類識別特征提取分類識別
    2DPCA(120×4)[8]16.65870.88937.58869.93572.75368.149107.94166.383143.17964.622
    RC2DPCA(8×8)[11]16.73611.35438.19911.42373.97711.227109.86710.805145.76410.612
    Angle-2DPCA(120×4)[12]16.09648.77416.09648.11816.08046.76816.08045.59616.08044.440
    BA2DPCA(8×8)[13]16.08021.64216.08821.14216.10420.5716.1220.50416.09620.036
    BDPCA(8×8)[10]16.37911.34537.04011.42771.66311.243106.43810.768141.16010.620
    IBDPCA(8×8)[17]8.83011.3828.49911.4038.48211.2188.47810.7808.48610.612
    I2DPCA(120×4)8.84770.8978.50369.9358.48668.1418.48666.3018.50364.602
    IRC2DPCA(8×8)8.51111.3508.50711.4198.48611.2438.53610.7978.51110.604
    下載: 導出CSV
  • 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
    陳小龍, 關(guān)鍵, 于曉涵, 等. 基于短時稀疏時頻分布的雷達目標微動特征提取及檢測方法[J]. 電子與信息學報, 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
    王肖鋒, 張明路, 劉軍. 基于增量式雙向主成分分析的機器人感知學習方法研究[J]. 電子與信息學報, 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
    謝自強, 葛為民, 王肖鋒, 等. 發(fā)展型機器人實時特征提取方法研究[J]. 機器人, 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, 劉宏偉, 吳順君. 一種有效的增量BDPCA算法[J]. 系統(tǒng)仿真學報, 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
    文穎, 施鵬飛. 一種基于共同向量結(jié)合2DPCA的人臉識別方法[J]. 自動化學報, 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
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  • 收稿日期:  2018-12-10
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