Fast linear discriminant analysis using binary bases

Pattern Recognition Letters

Volumn 28, Issue 16, 2007, Pages 2209-2218

  Tang, Feng ^a   Tao, Hai ^a  

^a University of California Santa Cruz   (United States)

Author keywords

Image representations; Linear discriminant analysis; Non orthogonal binary subspace

Indexed keywords

DISCRIMINANT ANALYSIS; INTEGRAL EQUATIONS; VECTOR QUANTIZATION;

BASE VECTORS; IMAGE REPRESENTATIONS; LINEAR DISCRIMINANT ANALYSIS (LDA); NON-ORTHOGONAL BINARY SUBSPACE;

PATTERN RECOGNITION;

EID: 35148863980     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2007.07.007     Document Type: Article

References (23)

1. Andrle M., and Rebollo-Neira L. A swapping-based refinement of orthogonal matching pursuit strategies. Signal Process. 86 3 (2006) 480-495 Special Issue on Sparse Approximations in Signal and Image Processing
2. Belhumeur P.N., Hespanda J., and Kiregeman D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. PAMI 19 7 (1997) 711-720
3. Chen S., and Li D. Modified linear discriminant analysis. Pattern Recognition 38 3 (2005) 441-443
4. Davis G., Mallat S., and Avellaneda M. Greedy adaptive approximation. J. Construct. Approx. 13 1 (1997) 57-98
5. Gilbert, A.C., Muthukrishnan, S., Strauss, M., 2003. Approximation of functions over redundant dictionaries using coherence. In: Proc. 14th Annual ACM-SIAM Symposium on Discrete Algorithms 2003, pp. 243-252.
6. Huang, R., Liu, Q., Lu, H., Ma, S., 2002. Solving the small sample size problem of ldaf. In: ICPR02, pp. III: 29-32.
7. Jing, X.Y., Zhang, D., Yao, Y.F., 2003. Improvements on the linear discrimination technique with application to face recognition. 24(15), November 2003.
8. Ke, Y., Sukthankar, R., Hebert, M., 2005. Efficient visual event detection using volumetric features. In: ICCV, pp. I: 166-173.
9. Li M., and Yuan B. 2d-lda: A statistical linear discriminant analysis for image matrix. Pattern Recognition Lett. 26 5 (2005) 527-532
10. Lin, R.S., Yang, M.H., Levinson, S.E., 2004. Object tracking using incremental fisher discriminant analysis. In: ICPR, pp. 23-26.
11. Lu J., Plataniotis K.N., and Venetsanopoulos A. Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recognition Lett. 26 2 (2005) 181-191
12. Mallat S., and Zhang Z. Matching pursuit with time-frequency dictionaries. IEEE Trans. Signal Process. 41 12 (1993) 3397-3415
13. Mita, T., Kaneko, T., Hori, O., 2005. Joint haar-like features for face detection. In: ICCV, pp. II: 1619-1626.
14. Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S., 1993. Orthogonal matching pursuits: Recursive function approximation with applications to wavelet decomposition. In: Proc. 27th Asilomar Conf. on Signals, Systems, Computers, pp. 40-44.
15. Rebollo-Neira L., and Lowe D. Optimized orthogonal matching pursuit approach. IEEE Signal Processing Lett. 9 4 (2002) 137-140
16. Schweitzer, H., Bell, J.W., Wu, F., 2002. Very fast template matching. In: ECCV, pp. 358-372.
17. Swets D.L., and Weng J. Hierarchical discriminant analysis for image retrieval. PAMI 21 5 (1999) 386-401
18. Tang, F., Tao, H., 2006. Binary principal component analysis. In: British Machine Vision Conf., p. I: 377.
19. Tao, H., Crabb, R., Tang, F., 2005. Non-orthogonal binary subspace and its applications in computer vision. In: ICCV, pp. 864-870.
20. Veksler, O., 2003. Fast variable window for stereo correspondence using integral images. In: CVPR, pp. I: 556-561.
21. Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. I: 511-518.
22. Viola, P., Jones, M., Snow, D., 2003. Detecting pedestrians using patterns of motion and appearance. In: ICCV, pp. II: 734-741.
23. Zhuang X., and Dai D. Inverse fisher discriminate criteria for small sample size problem and its application to face recognition. Pattern Recognition 38 11 (2005) 2192-2194