Two Dimension Linear Discriminant Analysis Based on Image Sampling and Regroupment
基于图像抽样重组的2维线性鉴别分析

The columns or rows of an image are practically viewed as sample vectors in two dimension linear discriminent analysis (2DLDA). However, those sample vectors can not fulfill the independent identically distributed requirement in statistics. This paper proposes a method, called Sampling and Regroupment 2DLDA (SR2DLDA), which can improve 2DLDA and LDA. SR2DLDA down-samples the sample images, regroups the small down-sampling images to matrices, and then performs 2DLDA on them. These matrices may make progress on the independent identically distributed requirement. The experiments on ORL database, UMIST database and FERET database verify the efficiency of the SR2DLDA.