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面向图像识别的测地局部典型相关分析方法

许欢 苏树智 颜文婧 邓瀛灏 谢军

许欢, 苏树智, 颜文婧, 邓瀛灏, 谢军. 面向图像识别的测地局部典型相关分析方法[J]. 电子与信息学报, 2020, 42(11): 2813-2818. doi: 10.11999/JEIT200123
引用本文: 许欢, 苏树智, 颜文婧, 邓瀛灏, 谢军. 面向图像识别的测地局部典型相关分析方法[J]. 电子与信息学报, 2020, 42(11): 2813-2818. doi: 10.11999/JEIT200123
Huan XU, Shuzhi SU, Wenjing YAN, Yinghao DENG, Jun XIE. A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2813-2818. doi: 10.11999/JEIT200123
Citation: Huan XU, Shuzhi SU, Wenjing YAN, Yinghao DENG, Jun XIE. A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2813-2818. doi: 10.11999/JEIT200123

面向图像识别的测地局部典型相关分析方法

doi: 10.11999/JEIT200123
基金项目: 国家自然科学基金(61806006),安徽省高等学校自然科学研究基金(KJ2018A0083),中国博士后科学基金(2019M660149)
详细信息
    作者简介:

    许欢:女,1982年生,助教,研究方向为机器学习、图像处理、模式识别

    苏树智:男,1987年生,副教授,研究方向为多模态模式识别、特征学习、子空间融合、图像处理

    颜文婧:女,1984年生,讲师,研究方向为机器学习、模式识别、信号处理

    通讯作者:

    苏树智 sushuzhi@foxmail.com

  • 中图分类号: TN911.73; TP391.4

A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition

Funds: The National Natural Science Foundation of China (61806006), The Anhui Province Natural Science Research Foundation of Institutions of Higher Learning (KJ2018A0083), The China Postdoctoral Science Foundation (2019M660149)
  • 摘要: 典型相关分析(CCA)是一种经典的多模态特征学习方法,能够从不同模态同时学习相关性最大的低维特征,然而难以发现隐藏在样本空间中的非线性流形结构。该文提出一种基于测地流形的多模态特征学习方法,即测地局部典型相关分析(GeoLCCA)。该方法利用测地距离构建了低维相关特征的测地散布,并进一步通过最大化模态间的相关性和最小化模态内的测地散布学习更具鉴别力的非线性相关特征。该文不仅在理论上对提出的方法进行了分析,而且在真实的图像数据集上验证了方法的有效性。
  • 表  1  在GT图像数据集上的识别率(%)及标准差

    训练样本数5训练样本数6训练样本数7训练样本数8
    GeoLCCA67.26±2.0171.36±1.8376.10±1.2878.20±1.31
    GMCCA65.22±1.6466.64±1.5669.70±1.7572.06±1.66
    LPCCA44.84±1.7350.09±3.7954.15±1.7457.46±2.56
    DMCCA63.56±2.7767.80±1.2973.67±1.7175.80±1.99
    CCA59.08±1.8161.78±1.3566.22±1.6668.14±2.01
    A±B: A表示平均识别率(%),B表示对应的识别率标准差
    下载: 导出CSV

    表  2  在ORL图像数据集上的识别率(%)及标准差

    训练样本数5训练样本数6训练样本数7训练样本数8
    GeoLCCA95.15±1.5897.19±1.3398.25±0.8399.50±0.65
    GMCCA93.90±2.0495.19±0.8997.00±1.5398.50±1.42
    LPCCA84.70±3.0087.81±2.4089.17±2.0094.25±2.58
    DMCCA93.80±1.5395.50±1.7496.75±1.4999.38±0.66
    CCA90.35±1.9493.19±1.9493.83±1.6897.25±1.15
    A±B: A表示平均识别率(%),B表示对应的识别率标准差
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-21
  • 修回日期:  2020-07-23
  • 网络出版日期:  2020-07-23
  • 刊出日期:  2020-11-16

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