Feature Extraction: Algebraic and Geometrical Perspectives
Xiao‐Jun Wu · 2014
In this talk, several feature extraction techniques will be introduced from two cues: algebraic feature extraction andgeometrical feature extraction methods. For algebraic feature extraction, Principal Component Analysis (PCA), LinearDiscriminant Analysis (LDA), Kernel Discriminant Analysis (KDA), Sparse representation and their variants will beintroduced. For geometrical feature extraction, Contour Points Distribution Histogram (CPDH), Active Shape Model (ASM),Active Appearance Model (AAM), ASM+AAM, and their variants will be presented. Typical applications of these featureextraction methods will be presented including face recognition, image fusion, image retrieval, and target tracking, etc. Themain aim of this talk is to introduce my main work in pattern recognition and computer vision, which hopefully ignitesdiscussions, and future development of feature extraction.