Feature fusion: Parallel strategy vs. serial strategy

Jian Yang, Jing Yu Yang, Dapeng Zhang, Jian Feng Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

400 Citations (Scopus)


A new strategy of parallel feature fusion is introduced in this paper. A complex vector is first used to represent the parallel combined features. Then, the traditional linear projection analysis methods, including principal component analysis, K-L expansion and linear discriminant analysis, are generalized for feature extraction in the complex feature space. Finally, the developed parallel feature fusion methods are tested on CENPARMI handwritten numeral database, NUST603 handwritten Chinese character database and ORL face image database. The experimental results indicate that the classification accuracy is increased significantly under parallel feature fusion and also demonstrate that the developed parallel fusion is more effective than the classical serial feature fusion.
Original languageEnglish
Pages (from-to)1369-1381
Number of pages13
JournalPattern Recognition
Issue number6
Publication statusPublished - 1 Jun 2003


  • Character recognition
  • Complex feature space
  • Face recognition
  • Feature extraction
  • Feature fusion
  • K-L expansion
  • Linear discriminant analysis (LDA)
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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