Kernel Feature Extraction Approach for Color Image Recognition

摘要：

　　Color Image Recognition is one of the most important fields in Pattern Recognition.Both Multi-set canonical correlation analysis and Kernel method are important techniques in the field of color image recognition.In this paper,we combine the two methods and propose one novel color image recognition approach:color image kemel canonical correlation analysis (CIKCCA).Color image kernel canonical correlation analysis is based on the theory of multi-set canonical correlation analysis and extracts canonical correlation features among the color image components.Then fuse the features of the color image components in the feature level,which are used for classification and recognition.Experimental results on the FRGC-v2 public color image databases demonstrate that the proposed approach acquire better recognition performance than other color recognition methods.

关键词： color image feature extraction kernel method canonical correlation analysis discriminant analysis

作者: Xiaoyuan Jing Kun Li Songsong Wu Yongfang Yao Chao Wang

作者单位: College of Automation,Nanjing University of Posts and Telecommunications,Nanjing,China;State Key Lab College of Automation,Nanjing University of Posts and Telecommunications,Nanjing,China

会议类型: 国际会议

会议名称: 2013 2nd international Conference on Opto-Electronics Engineering and Materials Eesearch(2013第二届光电工程与材料研究国际会议)(OEMR2013)

会议地点: 郑州

会议语种:英文

页码: 1159-1164

在线出版日期: 2013-10-19（万方平台首次上网日期，不代表论文的发表时间）