2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

关于二维主成分分析方法的研究

王立威 王潇 常明 封举富

王立威, 王潇, 常明, 封举富. 关于二维主成分分析方法的研究. 自动化学报, 2005, 31(5): 782-787.
引用本文: 王立威, 王潇, 常明, 封举富. 关于二维主成分分析方法的研究. 自动化学报, 2005, 31(5): 782-787.
WANG Li-Wei, WANG Xiao, CHANG Ming, FENG Ju-Fu. Is Two-dimensional PCA a New Technique?. ACTA AUTOMATICA SINICA, 2005, 31(5): 782-787.
Citation: WANG Li-Wei, WANG Xiao, CHANG Ming, FENG Ju-Fu. Is Two-dimensional PCA a New Technique?. ACTA AUTOMATICA SINICA, 2005, 31(5): 782-787.

关于二维主成分分析方法的研究

详细信息
    通讯作者:

    王立威

Is Two-dimensional PCA a New Technique?

More Information
    Corresponding author: WANG Li-Wei
  • 摘要: The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human face recognition. Numerous algorithms tried to generalize PCA in different aspects. More recently, a technique called two-dimensional PCA (2DPCA) was proposed to cut the computational cost of the standard PCA. Unlike PCA that treats images as vectors, 2DPCA views an image as a matrix. With a properly defined criterion, 2DPCA results in an eigenvalue problem which has a much lower dimensionality than that of PCA. In this paper, we show that 2DPCA is equivalent to a special case of an existing feature extraction method, i.e., the block-based PCA. Using the FERET database, extensive experimental results demonstrate that block-based PCA outperforms PCA on datasets that consist of relatively simple images for recognition, while PCA is more robust than 2DPCA in harder situations.
  • 加载中
计量
  • 文章访问数:  3053
  • HTML全文浏览量:  68
  • PDF下载量:  1735
  • 被引次数: 0
出版历程
  • 收稿日期:  2004-04-05
  • 修回日期:  2005-06-06
  • 刊出日期:  2005-09-20

目录

    /

    返回文章
    返回