Joint geometry and variability for image recognition

Neurocomputing

Volumn 99, Issue , 2013, Pages 241-249

  Gao, Quanxue ^a   Zhang, Haijun ^a   Yang, Xiaojing ^a   Liu, Jingjing ^a   Liu, Yamin ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Dimensionality reduction; Geometry; NPE; Variability

Indexed keywords

ADJACENCY GRAPHS; DIMENSIONALITY REDUCTION; GEOMETRICAL STRUCTURE; HIGH DIMENSIONAL DATA; JOINT GEOMETRY; LINEAR APPROACH; NEIGHBORHOOD PRESERVING EMBEDDING; NPE; OBJECTIVE FUNCTIONS; REAL-WORLD IMAGE; TRAINING DATA; VARIABILITY; VARIABILITY ANALYSIS;

IMAGE RECOGNITION;

GEOMETRY;

ACCURACY; ALGORITHM; ANALYTIC METHOD; ARTICLE; DATA BASE; DISCRIMINANT ANALYSIS; EMBEDDING; IMAGE ANALYSIS; IMAGE QUALITY; JOINT GEOMETRY AND VARIABILITY ANALYSIS; NEIGHBORHOOD PRESERVING EMBEDDING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

EID: 84867873734     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.06.027     Document Type: Article

References (46)

1. Jolliffe I.T. Principal Component Analysis 1986, Springer-Verlag, New York.
2. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press. 2nd edition.
3. Strassen V. Gaussian elimination is not optimal. Numer. Math. 1969, 13:54-356.
4. Tao D., Li X., Wu X., Maybank S.J. General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14, 2007, 29(10):1700-1717.
5. Saul L.K., Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 2003, 4:119-155.
6. Roweis S., Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290(5500):2323-2326.
7. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 2003, 15(6):1373-1396.
8. He X., Yan S., Hu Y., Niyogi P., Zhang H. Face recognition using laplacian faces. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(3):328-340.
9. K.Q. Weinberger, L.K. Saul, Unsupervised learning of image manifolds by semi-definite programming, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004, 988-995.
10. K.Q. Weinberger, B.D. Packer, L.K. Saul, Nonlinear dimensionality reduction by semi-definite programming and kernel matrix factorization, in: Proceedings of the Tenth Workshop Artificial Intelligence and Statistics (AISTATS-2005), 381-388.
11. Xia T., Tao D., Mei T., Zhang Y. Multiview spectral embedding. IEEE Trans. Syst. Man Cybern.: Part B: Cybern. 2010, 40(6):1438-1446.
12. Gao Q., Xu H., Li Y., Xie D. Two-dimensional supervised local similarity and diversity projection. Pattern Recognition 2010, 43(10):3359-3363.
13. He X., Cai D., Yan S., Zhang H. Neighborhood preserving embedding. in Proceedings of IEEE International Conference on Computer Vision 2005.
14. Lai Z., Wan M., Jin Z., Yang J. Sparse two-dimensional local discriminant projections for feature extraction. Neurocomputing 2011, 74:629-637.
15. Yu J., Liu D., Tao D., Seah H.S. Complex object corresponding construction in two- dimensional animation. IEEE Trans. Image Processing 2011, 20(11):3257-3269.
16. Xu Y., Feng G., Zhao Y. One improvement to two-dimensional locality preserving projection method for use with face recognition. Neurocomputing 2009, 73:245-249.
17. Tao D., Li X., Wu X., Maybank S.J. Geometric Mean for Subspace Selection. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(2):260-274.
18. Wang X., Li Z., Tao D. Subspace indexing model on grassmann manifold for image search. IEEE Trans. Image Processing 2011, 20(9):2627-2635.
19. Yan S., Xu D., Zhang B., Zhang H., Yang Q., Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(1):40-51.
20. X. He, D. Cai, P. Niyogi, Tensor subspace analysis, in: Proceedings of the Neural Information Processing Systems (NIPS'05), 2005.
21. Kokiopoulou E., Saad Y. Orthogonal neighborhood preserving projections: a projection-based dimensionality reduction technique. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(12):2143-2156.
22. Zhang T., Tao D., Li X., Yang J. Patch alignment for dimensionality reduction. IEEE Trans Knowl. Data Eng. 2009, 21(9):1299-1313.
23. Zhang T., Huang K., Li X., Yang J., Tao D. Discriminative orthogonal neighborhood-preserving projections for classification. IEEE Trans. Syst. Man, Cybern.: Part B 2010, 40(1):253-263.
24. Qiao L., Chen S., Tan X. Sparsity preserving projections with applications to face recognition. Pattern Recognition 2010, 43:331-341.
25. X. Tian, D. Tao, Sparse transfer learning for interactive video search reranking, ACM Trans. Multimedia Comput. Commun. Appl., 2012.
26. Xie B., Mu Y., Tao D., Huang K. m-SNE: multiview stochastic neighbor embedding. IEEE Trans. Syst. Man Cybern.: Part B: Cybern. 2011, 41(6):1088-1096.
27. Lu K., Zhao J. Neighborhood preserving regression for image retrieval. Neurocomputing 2011, 74:1467-1473.
28. Zhou T., Tao D., Wu X. Manifold elastic net: a unified framework for sparse dimension reduction. Data Min. Knowl. Disc. 2011, 22(3):340-371.
29. Gao Q., Zhang H., Liu J. Two-dimensional margin, similarity and variation embedding. Neurocomputing 2012, 86(6):179-183.
30. Gao Q., Liu J., Zhang H., Hou J., Yang X. Enhanced fisher discriminant criterion for image recognition. Pattern Recognition 2012, 45(10):3717-3724.
31. Bian W., Tao D. Max-min distance analysis by using sequential SDP relaxation for dimension reduction. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(5):1037-1050.
32. Cox T., Cox M. Multidimensional Scaling 1994, Chapman & Hall, London.
33. Tenenbaum J.B., de Silva V., Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
34. Turk M., Pentland A. Eigenfaces for recognition. J. Cogn. Neurosci. 1991, 3(1):71-86.
35. Cai D., He X., Han J. Isometric projection. in Proceedings of 22nd Conference on Artificial Intelligence 2007, 528-533.
36. Belhumeur P., Hespanha P., Kriegman D. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 19:711-720.
37. Yang J., Zhang D., Frangi A.F., Yang J.Y. Two-Dimensional P.C.A.: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal. Mach. Intell. 2004, 26(1):131-137.
38. Phillips P., Moon H., Rizvi S., Rauss P. The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(10):1090-1104.
39. COIL20 database,. http://www1.cs.columbia.edu/CAVE/software/softlib/coil-20.php.
40. Guan N., Tao D., Luo Z., Yuan B. Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent. IEEE Trans. Image Processing 2011, 20(7):2030-2048.
41. J. Yang, S. Yang, Y. Fun, X. Li, T. Huang, Non-negative graph embedding, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
42. Guan N., Tao D., Luo Z., Yuan B. Non-negative patch alignment framework. IEEE Trans. Neural Networks 2011, 22(8):1218-1230.
43. Tao D., Li X., Hu W., Maybank S., Wu X. Supervised tensor learning. Knowledge and Information Systems 2007, 13(1):1-42.
44. Zhang L., Chen C., Bu J., Cai D., He X., Huang T.S. Active learning based on locally linear reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(10):2026-2038.
45. N. Guan, D. Tao, Z. Luo, B. Yuan, Online non-negative matrix factorization with robust stochastic approximation. IEEE Trans. Neural Networks. Learn. Syst. 23 (7) (2012) 1087-1099.
46. Guan N., Tao D., Luo Z., Yuan B. NeNMF: an optimal gradient method for nonnegative matrix factorization. IEEE Trans. Signal Processing 2012, 60(6):2882-2898.