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 journalArticle

5 Citations (Scopus)

Abstract

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
Volume75
Issue number17
DOIs
Publication statusPublished - 1 Sep 2016

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