2D-LDA: A statistical linear discriminant analysis for image matrix
By Ming Li, Baozong Yuan. Pattern Recognition Letters, 2005
706 citations recorded by the source corpus.
This paper proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher's Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/c21ebd4b58737480899c8b36f0371eeb68b68e96
How researchers see this paper
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In Artificial Intelligence
- Hamid Reza Arabnia
- Juergen Gall
- Christian Bauckhage
- Muhammed S. Cheema
- Rabia Jafri
- Abdalrahman Eweiwi
- Luc Van Gool
- Alexander Richard
In Computer Vision and Pattern Recognition
- Abdalrahman Eweiwi
- Christian Bauckhage
- Muhammed S. Cheema
- Juergen Gall
- Hamid Reza Arabnia
- Rabia Jafri
- Luc Van Gool
- Bernt Schiele
Citations in the corpus
- Efficient Pose-Based Action Recognition (2014, background): “While PLS relies only on the between-class covariance matrix [26], which results in (8), LDA also takes the intra-class covariances for each class into account.”
- A Survey of Face Recognition Techniques (2009, methodology): “…[111], Discriminant Local Feature Analysis [112], Kernel PCA/LDA [113, 114], Kernel Scatter-Difference-based Discriminant Analysis [115], 2D-LDA [116, 117], Fourier-LDA [118], Gabor-LDA [119], Block LDA [120], Enhanced FLD [121], Component-based Cascade LDA [122], and incremental LDA [123], to…”