Optimal feature selection for subspace image matching

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Volumn , Issue , 2009, Pages 200-205

  Roig, Gemma ^a   Boix, Xavier ^b   De La Torre, Fernando ^c  

^a UNIVERSITAT RAMON LLULL   (Spain)

^b Campus UAB ^*  (Spain)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX RELAXATION; EIGENVALUE PROBLEM; ENERGY MINIMIZATION; FACIAL FEATURE DETECTION; FEATURE SELECTION; GRADIENT-DESCENT; GREEDY ALGORITHMS; IMAGE FEATURES; NP-HARD; OPTIMIZATION CRITERIA; OPTIMIZATION STRATEGY; RESEARCH TOPICS; SELECTION MECHANISM; SPARSE SET; SUBSPACE MODELS;

COMPUTER VISION; EIGENVALUES AND EIGENFUNCTIONS; IMAGE MATCHING; OPTIMIZATION; QUADRATIC PROGRAMMING; RELAXATION PROCESSES;

FEATURE EXTRACTION;

EID: 77953185180     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2009.5457698     Document Type: Conference Paper

References (24)

1. S. Avidan. Joint feature-basis subset selection. In CVPR, 2004.
2. S. Belongie, J. Malik, and J. Puzicha. Shape context: A new descriptor for shape matching and object recognition. In Advances in Neural Infor. Processing Systems, 2000.
3. S. Belongie, J. Malik, and J. Puzicha. Matching shapes. In ICCV, pages 454-461, 2001.
4. P. J. Besl and H. D. Mckay. A method for registration of 3-d shapes. PAMI, 14(2):239-256, 1992.
5. B. Cao, D. Shen, J.-T. Sun, Q. Yang, and Z. Chen. Feature selection in a kernel space. In International Conference on Machine Learning, 2007.
6. T. Cootes and C. Taylor. Statistical models of appearance for computer vision. In Tech. Report. U. of Manchester, 2001.
7. F. de la Torre and T. Kanade. Discriminative cluster analysis. In International Conference on Machine Learning, 2006.
8. R. Fletcher. Practical methods of optimization. Wiley, 1987.
9. R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker. The cmu multi-pose, illumination, and expression (multipie) face database. Technical report, Carnegie Mellon University Robotics Institute.TR-07-08, 2007.
10. C. Harris and M. Stephens. A combined corner and edge detection. In Proceedings of The Fourth Alvey Vision Conference, pages 147-151, 1988.
11. L. Hermes and J. Buhmann. Feature selection for support vector machines. In ICPR, 2000.
12. I. T. Jolliffe. Principal Component Analysis. New York: Springer-Verlag, 1986.
13. K. Kira and L. A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In AAAI, pages 129-134, 1992.
14. M. Leordeanu and M. Hebert. A spectral technique for correspondence problems using pairwise constraints. In ICCV, pages 1482-1489, 2005.
15. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
16. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. PAMI, 27(10):1615-1630, 2005.
17. B. Moghaddam, A. Gruber, Y. Weiss, and S. Avidan. Sparse regression as a sparse eigenvalue problem. Information Theory and Applications Workshop, pages 121-127, February 2008.
18. M. C. Plateau. Quadratic convex reformulations for quadratic 0-1 programming. 4OR: A Quarterly Journal of Operations Research, 6(2):187-190, June 2008.
19. A. Rangarajan, F. Chui, and H. Bookstein. The softassign procrustes matching algorithm. Information Processing in Medical Imaging, pages 29-42, 1997.
20. S. Sclaroff and A. Pentl. Modal matching for correspondence and recognition. PAMI, 17:545-561, 1995.
21. G. L. Scott and H. C. Longuet-Higgins. An algorithm for associating the features of two images. In Proc. Royal Society London, volume B244, pages 21-26, 1991.
22. P. H. S. Torr. Geometric motion segmentation and model selection. Phil. Trans. Royal Society of London, 1998.
23. L. Torresani, V. Kolmogorov, and C. Rother. Feature correspondence via graph matching: Models and global optimization. ECCV, pages 596-609, 2008.
24. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001.