On image matrix based feature extraction algorithms

@article{Wang2006OnIM,
  title={On image matrix based feature extraction algorithms},
  author={Liwei Wang and Xiao Wang and Jufu Feng},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  year={2006},
  volume={36},
  pages={194-197}
}
Principal component analysis (PCA) and linear discriminant analysis (LDA) are two important feature extraction methods and have been widely applied in a variety of areas. A limitation of PCA and LDA is that when dealing with image data, the image matrices must be first transformed into vectors, which are usually of very high dimensionality. This causes expensive computational cost and sometimes the singularity problem. Recently two methods called two-dimensional PCA (2DPCA) and two-dimensional… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 53 CITATIONS, ESTIMATED 28% COVERAGE

189 Citations

0102030'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 189 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 19 REFERENCES

The equivalence of twodimensional PCA to line-based PCA

  • L. Wang, X. Wang, X. Zhang, J. Feng
  • Pattern Recognit. Lett., vol. 26, no. 1, pp. 57…
  • 2005
1 Excerpt

Face recognition using the embedded HMM with second-order block-specific observations

  • M. Kim, D. Kim, S. Lee
  • Pattern Recognit., vol. 36, no. 11, pp. 2723–2735…
  • 2003
1 Excerpt

Similar Papers

Loading similar papers…