Acceleration strategies for Gaussian mean-shift image segmentation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 1, Issue , 2006, Pages 1160-1167

  Carreira Perpiñán, Miguel Á ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION STRATEGIES; EXPECTATION-MAXIMISATION (EM); GAUSSIAN MEAN-SHIFT (GMS);

ALGORITHMS; GAUSSIAN NOISE (ELECTRONIC); ITERATIVE METHODS; OPTICAL RESOLVING POWER; VECTORS;

IMAGE SEGMENTATION;

EID: 33845577819     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2006.44     Document Type: Conference Paper

References (15)

1. M. Á. Carreira-Perpiñán. Mode-finding for mixtures of Gaussian distributions. IEEE Trans. PAMI, 22(11):1318-1323, Nov. 2000.
2. M. Á. Carreira-Perpiñán. Gaussian mean shift is an EM algorithm. Submitted, 2005.
3. M. Á. Carreira-Perpiñán and C. K. I. Williams. On the number of modes of a Gaussian mixture. In L. Griffin and M. Lillholm, editors, Scale Space Methods in Computer Vision, vol. 2695 of Lecture Notes in Comp. Sci., pages 625-640, 2003.
4. Y. Cheng. Mean shift, mode seeking, and clustering. IEEE Trans. PAMI, 17(8):790-799, Aug. 1995.
5. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. PAMI, 24(5):603-619, May 2002.
6. D. DeMenthon. Spatio-temporal segmentation of video by hierarchical mean shift analysis. In Statistical Methods in Video Processing Workshop (SMVP 2002), Copenhagen, Denmark, June 1-2 2002.
7. A. Elgammal, R. Duraiswami, and L. S. Davis. Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking. IEEE Trans. PAMI, 25(11):1499-1504, Nov. 2003.
8. K. Fukunaga and L. D. Hosteller. The estimation of the gradient of a density function, with application in pattern recognition. IEEE Trans. Info. Theory, IT-21(1):32-40, Jan. 1975.
9. B. Georgescu, I. Shimshoni, and P. Meer. Mean shift based clustering in high dimensions: A texture classification example. In ICCV, pages 456-463, 2003.
10. L. Greengard and J. Strain. The fast Gauss transform. SIAM J. Sci. Stat. Comput., 12(1):79-94, Jan. 1991.
11. G. J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. John Wiley & Sons, New York, 1997.
12. A. W. Moore. Very fast EM-based mixture model clustering using multiresolution kd-trees. In NIPS, pp. 543-549, 1999.
13. R. M. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. MIT Press, 1998.
14. C. Yang, R. Duraiswami, D. DeMenthon, and L. Davis. Mean-shift analysis using quasi-Newton methods. In ICIP, pages 447-450, 2003.
15. C. Yang, R. Duraiswami, N. A. Gumerov, and L. Davis. Improved fast Gauss transform and efficient kernel density estimation. In ICCV, pages 464-411, 2003.