Intrinsic mean shift for clustering on Stiefel and Grassmann manifolds

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 1896-1902

  Çetingül, Hasan Ertan ^a   Vidal, René ^a  

^a Whiting School of Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS;

ANALYTIC MANIFOLDS; COMPARATIVE STUDIES; COMPUTATION OF THE EXPONENTIAL; GRASSMANN MANIFOLD; ITERATIVE OPTIMIZATION; MEAN SHIFT ALGORITHM; NON-PARAMETRIC DENSITY ESTIMATORS; OBJECT CATEGORIZATION;

COMPUTER VISION;

EID: 70450189959     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206806     Document Type: Conference Paper

References (30)

1. A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra. Clustering on the unit hypersphere using von Mises-Fisher distributions. Journal of Machine Learning Research, 6: 1345-1382, 2005.
2. E. Begelfor and M. Werman. How to put probabilities on homographies. IEEE Trans. on PAMI, 27(10): 1666-1670, 2005.
3. S. Birchfield and S. Rangarajan. Spatiograms vs histograms for region-based tracking. In CVPR, volume II, pages 1158-1163, 2005.
4. M. Carreira-Perpiñán. Gaussian mean-shift is an EM algorithm. IEEE Trans. on PAMI, 29(5): 767-776, 2007.
5. Y. Cheng. Mean shift, mode seeking, and clustering. IEEE Trans. on PAMI, 17(8): 790-799, 1995.
6. Y. Chikuse. Statistics on Special Manifolds. Lecture Notes in Statistics. Springer, New York, 2003.
7. D. Comaniciu and P. Meer. Mean Shift: A robust approach toward feature space analysis. IEEE Trans. on PAMI, 24(5): 603-619, 2002.
8. D. Comaniciu, V. Ramesh, and P. Meer. The variable bandwidth mean shift and data-driven scale selection. In ICCV, volume 1, pages 438-445, 2001.
9. D. Comaniciu, V. Ramesh, and P. Meer. kernel-based object tracking. IEEE Trans. on PAMI, 25(5): 564-577, 2003.
10. I. Dhillon and D. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1): 143-175, 2001.
11. A. Edelman, T. Arias, and S. T. Smith. The geometry of algorithms with orthogonality constraints. SIAM Journal of Matrix Analysis Applications, 20(2): 303-353, 1998.
12. A. Elgammal, R. Duraiswami, and L. Davis. Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking. IEEE Trans. on PAMI, 25(11): 1499-1504, 2003.
13. M. Fashing and C. Tomasi. Mean shift is a bound optimization. IEEE Trans. on PAMI, 27(3): 471-474, 2005.
14. K. Fukunaga and L. Hostetler. The estimation of the gradient of a density function with application in pattern recognition. IEEE Trans. on Information Theory, pages 32-40, 1975.
15. B. Georgescu, I. Shimshoni, and P. Meer. Mean shift based clustering in high dimensions: A texture classification example. In ICCV, volume 1, pages 456-463, 2003.
16. H. Gong, C. Pan, Q. Yang, H. Lu, and S. Ma. A semi-supervised framework for mapping data to the intrinsic manifold. In ICCV, volume 1, pages 98-105, 2005.
17. O. Hamsici and A. Martinez. Spherical-homoscedastic distributions: The equivalency of spherical and normal distributions in classification. Journal of Machine Learning Research, 8: 1583-1623, 2007.
18. B. Leibe and B. Schiele. Analyzing appearance and contour based methods for object categorization. In CVPR, volume 2, pages 409- 415, 2003.
19. X. Li, Z. Hu, and F. Wu. A note on the convergence of the mean shift. Pattern Recognition, 40: 1756-1762, 2007.
20. S. Oba, K. Kato, and S. Ishii. Multi-scale clustering for gene expression profiling data. In IEEE Symposium on Bioinformatics and Bioengineering, pages 210-217, 2005.
21. S. Paris and F. Durand. A topological approach to hierarchical segmentation using mean shift. In CVPR, 2007.
22. M. Park, Y. Liu, and R. Collins. Efficient mean shift belief propagation for vision tracking. In CVPR, 2008.
23. Y. Sheikh, E. Khan, and T. Kanade. Mode-seeking by medoidshifts. In ICCV, 2007.
24. R. Subbarao and P. Meer. Nonlinear mean shift for clustering over analytic manifolds. In CVPR, volume 1, pages 1168-1175, 2006.
25. R. Tron and R. Vidal. A benchmark for the comparsion of 3-D motion segmentation algorithms. In CVPR, 2007.
26. P. Turaga, A. Veeraraghavan, and R. Chellappa. Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision. In CVPR, 2008.
27. O. Tuzel, R. Subbarao, and P. Meer. Simultaneous multiple 3D motion estimation via mode finding on Lie groups. In ICCV, volume 1, pages 18-25, 2005.
28. A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In ECCV, pages 705-718, 2008.
29. A. Yilmaz. Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection. In CVPR, 2007.
30. X. Yuan and S. Li. Half quadratic analysis for mean shift: with extension to a sequential data mode-seeking method. In ICCV, 2007.