Title :
A novel statistical linear discriminant analysis for image matrix: two-dimensional Fisherfaces
Author :
Li, Ming ; Yuan, Baozong
Author_Institution :
Inst. of Inf. Sci., Beijing Jiaotong Univ., China
fDate :
31 Aug.-4 Sept. 2004
Abstract :
In the pattern recognition field, how to extract the proper features is a very important problem. In recent year, the statistical methods have been researched widely and many methods for feature extraction have been developed, such as, PCA, ICA, nonlinear PCA and etc. But the image always need be transformed to a ID vector in the traditional statistical methods. This paper proposed a novel linear discriminant analysis for image matrix, which achieved better result than the traditional ones. Experiments also proof our method is effective.
Keywords :
S-matrix theory; feature extraction; image representation; statistical analysis; feature extraction; image matrix; image representation; linear discriminant analysis; pattern recognition; statistical method; Covariance matrix; Eigenvalues and eigenfunctions; Equations; Image resolution; Linear discriminant analysis; Principal component analysis; Scattering; Vectors;
Conference_Titel :
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN :
0-7803-8406-7
DOI :
10.1109/ICOSP.2004.1441592