Extensions of principle component analysis with applications on vision based computing

Charles Z. Liu*, Manolya Kavakli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper mainly focuses on the principle component analysis (PCA) and its applications on vision based computing. The underlying mechanism of PCA given and several significant factors, involved with subspace training are discussed theoretically in detail including principle components energy, residuals assessment, and decomposition computation. The typical extensions, including probabilistic PCA (PPCA), kernel PCA (KPCA), multi-dimensional PCA and robust PCA (RPCA), have been presented with critical analysis on both mechanisms and computations. Combining with the studies on, such as, image compression, visual tracking, image recognition and super-resolution image reconstruction, PCA and its extensions applied to computer vision are critically reviewed and evaluated on the targeted issues in each application as well as the role they played at specific tasks to the characteristics, cost and suitable situations of each PCA based vision processing technique.

Original languageEnglish
Pages (from-to)10113-10151
Number of pages39
JournalMultimedia Tools and Applications
Issue number17
Early online date3 Nov 2015
Publication statusPublished - 1 Sept 2016

Bibliographical note

Publisher Copyright:
© 2015, Springer Science+Business Media New York.


  • Eigenspace
  • Extended PCA
  • Image compression
  • Image recognition
  • Karhunen-Loève transform
  • Principle component analysis
  • Subspace training
  • Super-resolution image reconstruction
  • Visual tracking


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