A-Optimal Projection for Image Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 38, Issue 5, 2016, Pages 1009-1015

  He, Xiaofei ^a   Zhang, Chiyuan ^a   Zhang, Lijun ^b   Li, Xuelong ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b NANJING UNIVERSITY   (China)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Dimensionality reduction; image representation; optimal design

Indexed keywords

IMAGE PROCESSING; LINEAR REGRESSION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS;

DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION ALGORITHMS; IMAGE REPRESENTATIONS; LOCALITY PRESERVING PROJECTIONS; LOW-DIMENSIONAL MANIFOLDS; LOW-DIMENSIONAL REPRESENTATION; OPTIMAL DESIGN; STATISTICAL EXPERIMENTAL DESIGN;

IMAGE RETRIEVAL;

EID: 84964545783     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2015.2439252     Document Type: Article

References (32)

1. X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, "Face recognition using Laplacianfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328-340, Mar. 2005
2. W. Liu, D. Tao, and J. Liu, "Transductive component analysis," in Proc. IEEE Int. Conf. Data Min., Pisa, Italy, 2008, pp. 433-442
3. D. Tao, X. Li, X. Wu, and S. J. Maybank, "Geometric mean for subspace selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 260-274, Feb. 2009
4. Y. Chen, J. Z. Wang, and R. Krovetz, "Content-based image retrieval by clustering," in Proc. 5th ACM SIGMM Int. Workshop Multimedia Information Retrieval, Berkeley, CA, USA, 2003, pp. 193-200
5. S. Gordon, H. Greenspan, and J. Goldberger, "Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations," in Proc. IEEE Int. Conf. Comput. Vis., Nice, France, 2003, pp. 370-377
6. X. He, W.-Y. Ma, and H.-J. Zhang, "Learning an image manifold for retrieval," in Proc. 12th Annu. ACM Conf. Multimedia, New York, NY, USA, Oct. 2004, pp. 17-23
7. W. Liu, W. Jiang, and S.-F. Chang, "Relevance aggregation projections for image retrieval," in Proc. ACM Int. Conf. Image Video Retrieval, Niagara Falls, Canada, 2008, pp. 119-126
8. D. Tao, X. Tang, X. Li, and Y. Rui, "Kernel direct biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm," IEEE Trans. Multimedia, vol. 8, no. 4, pp. 716-727, Aug. 2006
9. L. Zhang, C. Chen, J. Bu, Z. Chen, S. Tan, and X. He, "Discriminative codeword selection for image representation," in Proc. 18th ACM Int. Conf. Multimedia, 2010, pp. 173-182
10. Y.-Y. Lin, T.-L. Liu, and H.-T. Chen, "Semantic manifold learning for image retrieval," in Proc. 13th Annu. Int. ACM Conf. Multimedia, Singapore, Nov. 2005, pp. 249-258
11. J. Tenenbaum, V. de Silva, and J. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000
12. S. Roweis and L. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000
13. X. Li, S. Lin, S. Yan, and D. Xu, "Discriminant locally linear embedding with high-order tensor data," IEEE Trans. Syst., Man, Cybern., Part B, vol. 38, no. 2, pp. 342-352, Apr. 2008
14. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Proc. Adv. Neural Information Process. Syst. 14, Cambridge, MA, USA, 2001, pp. 585-591
15. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from examples," J. Mach. Learn. Res., vol. 7, pp. 2399-2434, 2006
16. A. C. Atkinson and A. N. Donev, Optimum Experimental Designs. London, U.K.: Oxford Univ. Press, 2007
17. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004
18. J. Nilsson, F. Sha, and M. I. Jordan, "Regression on manifolds using kernel dimension reduction," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 697-704
19. M. Kim and V. Pavlovic, "Dimensionality reduction using covariance operator inverse regression," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2008, pp. 1-8
20. A. Aswani, P. Bickel, and C. Tomlin, "Regression on manifolds: Estimation of the exterior derivative," Ann. Statist., vol. 39, no. 1, pp. 48-81, 2011
21. X. He and P. Niyogi, "Locality preserving projections," in Proc. Adv. Neural Inf. Process. Syst. 16, Cambridge, MA, USA, 2003, pp. 153-160
22. J. Yu and Q. Tian, "Learning image manifold by semantic subspace projection," in Proc. 14th Annu. Int. ACM Conf. Multimedia, Santa Barbara, CA, USA, Oct., 2006, pp. 297-306
23. F. R. K. Chung, Spectral Graph Theory (Series Regional Conference Series in Mathematics), Providence, RI, USA: AMS, vol. 92, 1997
24. D. Cai, X. He, and J. Han, "Spectral regression for efficient regularized subspace learning," in Proc. IEEE 11th Int. Conf. Comput. Vis., 2007, pp. 1-8
25. I. T. Jolliffe, "A note on the use of principal components in regression," J. Roy. Statist. Soc., vol. 31, no. 3, pp. 300-303, 1982
26. P. Geladi and B. R. Kowalski. (1986). Partial least-squares regression: A tutorial. Analytica Chimica Acta [Online]. 185, pp. 1-17. Available: http:// www.sciencedirect.com/science/article/pii/0003267086800289
27. K.-C. Li. (1991). Sliced inverse regression for dimension reduction. J. Am. Statist. Assoc. [Online]. 86(414), pp. 316-327. Available: http://www.jstor. org/stable/2290563
28. K. Fukumizu, F. R. Bach, and M. I. Jordan, "Kernel dimension reduction in regression," Ann. Statist., vol. 37, no. 4, pp. 1871-1905, 2009
29. L. Zhang, C. Chen, W. Chen, J. Bu, D. Cai, and X. He, "Convex experimental design using manifold structure for image retrieval," in Proc. 17th ACM Int. Conf. Multimedia, 2009, pp. 45-53
30. A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Contentbased image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000
31. H. Yu, M. Li, H.-J. Zhang, and J. Feng, "Color texture moments for contentbased image retrieval," in Proc. Int. Conf. Image Process., 2002, pp. 24-28
32. D. P. Huijsmans and N. Sebe, "How to complete performance graphs in content-based image retrieval: Add generality and normalize scope," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 245-251, Feb. 2005