摘要
把样本分布信息融于特征提取过程将有助于提高特征的分类能力.利用模糊隶属度概念,提出一种基于模糊标号典型相关分析的特征提取新方法.构造模糊标号刻画样本的分布情况,并将其与典型相关分析结合,能提取综合灰度信息和分布信息的有效判别特征.此外,针对样本不足导致的小特征值包含较多干扰信息的问题,基于矩阵理论及双空间分析思想,进一步提出双空间模糊标号典型相关分析算法,缓解了过小特征值对算法性能的影响.在ORL和组合人脸数据库上的实验结果表明新特征具有良好的分类能力,证实了所提算法的有效性及应用价值.
Incorporating the sample distribution information into the process of feature extraction is beneficial to promoting the classification performance of features. A fuzzy label canonical correlation analysis （CCA） algorithm is proposed for image feature extraction. Fuzzy class labels in the form of membership degrees are designed elaborately to represent the sample distribution. Then the fuzzy labels are embedded in CCA to extract more discriminative features which combine the information about gray level and distribution together. Furthermore, according to the matrix theory and dual-space idea, an improved method named dual-space fuzzy label CCA is proposed to counteract the effect of small eigenvalues which are poorly estimated clue to finite samples. The experimental results on ORL and combined face databases show that the features have a powerful ability of recognition, and that the proposed methods are efficient and practical.
出处
《大连理工大学学报》
EI
CAS
CSCD
北大核心
2009年第1期133-138,共6页
Journal of Dalian University of Technology
基金
新世纪优秀人才支持计划资助项目(NCET-05-0275)
国家自然科学基金资助项目(60673006,60873181)
关键词
典型相关分析
模糊隶属度
小样本问题
特征提取
人脸识别
canonical correlation analysis
fuzzy membership degree
small sample size problem
feature extraction
face recognition
