Visual object recognition using probabilistic kernel subspace similarity

Pattern Recognition

Volumn 38, Issue 7, 2005, Pages 997-1008

  Lee, Jianguo ^a   Wang, Jingdong ^b   Zhang, Changshui ^a   Bian, Zhaoqi ^a  

^a TSINGHUA UNIVERSITY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Kernel methods; Manifold analysis; Subspace analysis; Tangent distance; Visual object recognition

Indexed keywords

ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; POLYNOMIALS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; PROBABILITY DENSITY FUNCTION;

KERNEL METHODS; MANIFOLD ANALYSIS; SUBSPACE ANALYSIS; TANGENT DISTANCE; VISUAL OBJECT RECOGNITION;

OBJECT RECOGNITION;

EID: 17444417114     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.01.007     Document Type: Article

References (27)

1. S.K. Nayar, S.A. Nene, and H. Murase Subspace methods for robot vision IEEE Trans. Robot. Autom. 12 5 1996 750 758
2. A. Leonardis, and H. Bischof Kernel and subspace methods for computer vision Pattern Recognition 36 9 2003 1925 1927
3. W.S. Yambor, B.A. Draper, J.R. Beveridge, Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measure, in: Proceeding of the Second Workshop on Empirical Evaluation in Computer Vision, Dublin, Ireland, July 2000.
4. M.E. Tipping, and C.M. Bishop Probabilistic principal component analysis J. R. Stat. Soc. Ser. B 21 3 1999 611 622
5. B. Moghaddam, and A. Pentland Probabilistic visual learning for object representation IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 696 710
6. B. Moghaddam, T. Jebara, and A. Pentland Bayesian modeling of facial similarity M.J. Kearns S.A. Solla D.A. Cohn Advances in Neural Information Processing System 11 1999 MIT Press 910 916
7. B. Moghaddam Principal manifolds and probabilistic subspace for visual recognition IEEE Trans. Pattern Anal. Mach. Intell. 24 6 2002 780 788
8. P.J. Phillips, and H.J. Moon The FERET evaluation methodology for face recognition algorithms IEEE Trans. Pattern Anal. Mach. Intell. 22 10 2000 1090 1104
9. Y.W. Teh, and G.E. Hinton Rate-coded restricted Boltzmann machines for face recognition S.A. Solla T.K. Leen K.-R. Müller Advances in Neural Information Processing Systems 13 2001 MIT Press 908 914
10. X. Wang, X. Tang, Unified subspace analysis for face recognition, in: Proceedings of the IEEE International Conference on Computer Vision, 2003, pp. 679-686.
11. V. Vapnik The Nature of Statistical Learning Theory second ed. 1997 Springer New York
12. B. Schölkopf, and A.J. Smola Learning with Kernels 2002 MIT Press Cambridge, MA
13. M.L. Teixeira, The Bayesian Intrapersonal/Extrapersonal classifier, Masters Thesis, Computer Science Department, Colorado State University, July 2003.
14. P. Simard, Y. Le Cun, and J. Denker Efficient pattern recognition using a new transformation distance S.J. Hanson J.D. Cowan C.L. Giles Advances in Neural Information Processing Systems 5 1993 Morgan Kaufmann Press 50 58
15. P.Y. Simard, Y.A. LeCun, J.S. Denker, and B. Victorri Transformation invariance in pattern recognition - tangent distance and tangent propagation Int. J. Imag. System Technol. 11 3 2001 181 194
16. D. Keysers, W. Macherey, J. Dahmen, and H. Ney Learning of variability for invariant statistical pattern recognition Proceeding of the 12th European Conference on Machine Learning, Lecture Notes in Computer Science vol. 2167 2001 Springer Berlin 263 275
17. T.J. Hastie, P.Y. Simard, and E. Saeckinger Learning prototype models for tangent distance G. Tesauro D.S. Touretzky T.K. Leen Advances in Neural Information Processing Systems 7 1995 MIT Press 999 1006
18. H.S. Seung, and D.D. Lee The manifold ways of perception Science 290 2000 2268 2269
19. B. Schölkopf, A.J. Smola, and K.R. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10 5 1998 1299 1319
20. D. Cremers, T. Kohlberger, and C. Schnörr Shape statistics in kernel space for variational image segmentation Cremers Pattern Recognition 36 9 2003 1929 1943
21. J.B. Tenenbaum, V. Silva, and J.C. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 2000 2319 2323
22. G. Shakhnarovich, and B. Moghaddam Face recognition in subspaces S.Z. Li A.K. Jain Handbook of Face Recognition 2004 Springer Berlin
23. M. Turk, and A. Pentland Eigenfaces for recognition J. Cognitive Neurosci. 3 1 1991 71 86
24. V. Belhumeur, J. Hespanha, and D. Kriegman Eigenfaces vs. fisherfaces: recognition using class specific linear projection IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 711 720
25. M. Yang, N. Ahuja, D. Kriegman, Face recognition using kernel eigenfaces, in: Proceedings of the IEEE International Conference on Image Processing, 2000, pp. 37-40.
26. S.A. Nene, S.K. Nayar, H. Murase, Columbia object image library (COIL-20), Technical Report CUCS-005-96, Columbia University, February 1996.
27. J. Lee, J. Wang, C. Zhang, Z. Bian, Probabilistic tangent subspace: a unified view, in: Proceeding of the 21st International Conferences on Machine Learning (ICML 2004), Banff, Canada, July 2004.