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用于表示级特征融合与分类的相关熵融合极限学习机

吴超 李雅倩 张亚茹 刘彬

吴超, 李雅倩, 张亚茹, 刘彬. 用于表示级特征融合与分类的相关熵融合极限学习机[J]. 电子与信息学报, 2020, 42(2): 386-393. doi: 10.11999/JEIT190186
引用本文: 吴超, 李雅倩, 张亚茹, 刘彬. 用于表示级特征融合与分类的相关熵融合极限学习机[J]. 电子与信息学报, 2020, 42(2): 386-393. doi: 10.11999/JEIT190186
Chao WU, Yaqian LI, Yaru ZHANG, Bin LIU. Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification[J]. Journal of Electronics & Information Technology, 2020, 42(2): 386-393. doi: 10.11999/JEIT190186
Citation: Chao WU, Yaqian LI, Yaru ZHANG, Bin LIU. Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification[J]. Journal of Electronics & Information Technology, 2020, 42(2): 386-393. doi: 10.11999/JEIT190186

用于表示级特征融合与分类的相关熵融合极限学习机

doi: 10.11999/JEIT190186
基金项目: 国家自然科学基金(51641609)
详细信息
    作者简介:

    吴超:男,1990年生,博士生,研究方向为计算机视觉

    李雅倩:女,1982年生,副教授,研究方向为计算机视觉

    张亚茹:女,1995年生,博士生,研究方向为计算机视觉

    刘彬:男,1953年生,教授,研究方向为计算机视觉

    通讯作者:

    李雅倩 yaqianli@126.com

  • 中图分类号: TP391

Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification

Funds: The National Natural Science Foundation of China (51641609)
  • 摘要:

    在极限学习机(ELM)网络结构和训练模式的基础上,该文提出了相关熵融合极限学习机(CF-ELM)。针对多数分类方法中表示级特征融合不充分的问题,该文将核映射与系数加权相结合,提出了能够有效融合表示级特征的融合极限学习机(F-ELM)。在此基础上,用相关熵损失函数替代均方误差(MSE)损失函数,推导出用于训练F-ELM各层权重矩阵的相关熵循环更新公式,以增强其分类能力与鲁棒性。为了检验方法的可行性,该文分别在数据库Caltech 101, MSRC和15 Scene上进行实验。实验结果证明,该文所提CF-ELM能够在原有基础上进一步融合表示级特征,从而提高分类正确率。

  • 图  1  融合极限学习机的网络结构

    图  2  CF-ELM中循环次数对Caltech 101与MSRC数据库上正确率的影响曲线

    图  3  Caltech 101数据库上2种组合的正确率曲面图

    图  4  MSRC数据库上3种组合的正确率曲面图

    图  5  F-ELM与CF-ELM对两种数据库中具有嘈杂背景图像的正确率

    图  6  CF-ELM中循环次数对15 Scene数据库上正确率的影响曲线

    图  7  15 Scene数据库上3种组合的正确率曲面图

    图  8  F-ELM与CF-ELM对15 Scene的正确率

    表  1  Caltech 101与MSRC的正确率与训练时间

    组合方法SVMKELMF-ELMCF-ELM
    组合1组合2组合3组合1组合2组合3组合1组合2组合3组合1组合2组合3
    Caltech 101 (%)72.0477.9379.4378.8480.3180.1980.5983.65
    训练时间(s)862.16540.30860.37538.56861.76539.77902.13569.80
    MSRC (%)90.2688.5794.1391.4290.4291.7491.7490.5893.4990.9592.0695.76
    训练时间(s)79.5917.1117.1179.5117.0117.0179.5417.0417.0480.6818.2018.20
    下载: 导出CSV

    表  2  Caltech 101的结果比较

    方法SPM[9]LLC[17]文献[18]SDCD+PHOW[14]文献[19]ScSPM+DVM[20]文献[12]文献[8]文献[21]文献[16]CF-ELM-组合2
    字典维数20020481000102440010242048600400
    正确率(%)64.6073.4474.3075.3776.0077.7077.9378.0079.7083.9083.65
    下载: 导出CSV

    表  3  15 Scene的正确率与训练时间

    组合方法SVMKELMF-ELMCF-ELM
    组合1组合2组合4组合1组合2组合4组合1组合2组合4组合1组合2组合4
    15 Scene(%)74.3477.9286.4672.0080.7383.5377.2082.0684.3379.0083.0687.76
    训练时间(s)347.44106.25106.25347.12106.11106.11347.37106.41106.41367.50120.10120.10
    下载: 导出CSV

    表  4  15 Scene的结果比较

    方法SPM[9]LLC[17]SLC[22]LSVQ[22]文献[23]LGF[15]文献[12]MFS[24]文献[16]CF-ELM-组合4
    字典维数2001000102410241024400600400
    正确率(%)81.1081.7381.8983.0885.7085.8086.4687.1090.1087.76
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-27
  • 修回日期:  2019-09-03
  • 网络出版日期:  2019-09-12
  • 刊出日期:  2020-02-19

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