A Novel Feature Fusion Approach Based on Blocking and Its Application in Image Recognition

  • Xing Yan
  • Lei Cao
  • De-Shuang Huang
  • Kang Li
  • George Irwin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


According to the idea of canonical correlation analysis, a block-based method for feature extraction is proposed. The main process can be explained as follows: extract two groups of feature vectors from different blocks which belong to the same pattern; and then extract their canonical correlation features to form more effective discriminant vectors for recognition. To test this new approach, the experiment is performed on ORL face database and it shows that the recognition rate is higher than that of algorithm adopting single feature.


Feature Vector Recognition Accuracy Canonical Correlation Analysis Canonical Variate Feature Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xing Yan
    • 1
    • 3
  • Lei Cao
    • 2
  • De-Shuang Huang
    • 1
  • Kang Li
    • 4
  • George Irwin
    • 4
  1. 1.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHeFeiChina
  2. 2.Artillery Academy of People Liberation ArmyHeFeiChina
  3. 3.Department of AutomationUniversity of Science and Technology of ChinaHeFeiChina
  4. 4.School of Electrical & Electronic EngineeringQueen’s UniversityBelfast

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