Fast global kernel density mode seeking: Applications to localization and tracking

IEEE Transactions on Image Processing

Volumn 16, Issue 5, 2007, Pages 1457-1469

  Shen, Chunhua ^a,b   Brooks, Michael J ^a   van den Hengel, Anton ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b CSIRO   (Australia)

Author keywords

Annealing; Fast mean shift (MS); Global density mode; Visual localization; Visual tracking

Indexed keywords

BANDWIDTH; COMPUTATIONAL COMPLEXITY; PROBABILITY DENSITY FUNCTION; REAL TIME SYSTEMS; TARGET TRACKING;

FAST MEAN SHIFT; VISUAL LOCALIZATION; VISUAL TRACKING;

IMAGE PROCESSING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; MOTION; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VIDEORECORDING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MOTION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; VIDEO RECORDING;

EID: 34247329358     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2007.894233     Document Type: Article

References (45)

1. B. Georgescu, I. Shimshoni, and P. Meer, "Mean shift based clustering in high dimensions: A texture classification example," in Proc. IEEE Int. Conf. Computer Vision, Nice, France, 2003, vol. 2, pp. 456-463.
2. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564-577, May 2003.
3. R. Collins, "Mean-shift blob tracking through scale space," in Proc. IEEE Conf. Computer Vision Pattern Recognition, Madison, WI, Jun. 2003, vol. 2, pp. 234-240.
4. A. Elgammal, R. Duraiswami, and L. S. Davis, "Probabilistic tracking in joint feature-spatial spaces," in Proc. IEEE Conf. Computer Vision Pattern Recognition, Madison, WI, 2003, vol. 1, pp. 781-788.
5. G. D. Hager, M. Dewan, and C. V. Stewart, "Multiple kernel tracking with SSD," in Proc. IEEE Conf. Computer Vision Pattern Recognition Washington, DC, 2004, vol. 1, pp. 790-797.
6. H. Wang and D. Suter, "MDPE: A very robust estimator for model fitting and range image segmentation," Int. J. Comput. Vis., vol. 59, no. 2, pp. 139-166, 2004.
7. C. Yang, R. Duraiswami, and L. Davis, "Efficient spatial-feature tracking via the mean-shift and a new similarity measure," in Proc. IEEE Conf. Computer Vision Pattern Recognition, San Diego, CA, 2005, vol. 1, pp. 176-183.
8. K. Fukunaga and L. D. Hostetler, "The estimation of the gradient of a density function, with applications in pattern recognition," IEEE Trans. Inf. Theory, vol. IT-21, no. 1, pp. 32-40, Jan. 1975.
9. Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 8, pp. 790-799, Aug. 1995.
10. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002.
11. M. Fashing and C. Tomasi, "Mean shift is a bound optimization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 471-474, Mar. 2005.
12. S. Zhou, R. Chellappa, and B. Moghaddam, "Visual tracking and recognition using appearance-based modeling in particle filters," IEEE Trans. Image Process., vol. 13, no. 11, pp. 1491-1506, Nov. 2003.
13. E. Arnaud, E. Memin, and B. Cernuschi-Frias, "Conditional filters for image sequence-based tracking-Application to point tracking," IEEE Trans. Image Process., vol. 14, no. 1, pp. 63-79, Jan. 2005.
14. Z. Zivkovic and B. Krose, "An EM-like algorithm for color-histogram- based object tracking," in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2004, vol. 1, pp. 798-803.
15. Z. Fan, Y. Wu, and M. Yang, "Multiple collaborative kernel tracking," in Proc. IEEE Conf. Computer Vision Pattern Recognition, San Diego, CA, 2005, vol. 2, pp. 502-509.
16. P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, "Color-based probabilistic tracking," in Proc. Eur. Conf. Computer Vision, Copenhagen, Denmark, 2002, vol. 2350, pp. 661-675, Lecture Notes Comput. Sci..
17. P. van Laarhoven and E. Aarts, Simulated Annealing: Theory and Applications. New York: Springer Verlag, 1987.
18. R. M. Neal, "Annealed importance sampling," Statist. Comput., vol. 11, no. 2, pp. 125-139, Apr. 2001.
19. J. Deutscher and I. Reid, "Articulated body motion capture by stochastic search," Int. J. Comput. Vis., vol. 61, no. 2, pp. 185-205, 2005.
20. W. Härdle, M. Müller, S. Sperlich, and A. Werwatz, Nonparametric and Semiparametric Models, ser. Springer Ser. Statist. New York: Springer, 2004.
21. J. L. Bentley, "Multidimensional binary search trees used for associative searching," Commun. ACM, vol. 18, no. 9, pp. 509-517, Sep. 1975.
22. K. Zhang, M. Tang, and J. Kwok, "Applying neighborhood consistency for fast clustering and kernel density estimation," in Proc. IEEE Conf. Computer Vision Pattern Recognition, San Diego, CA, Jun. 2005, vol. 2, pp. 1001-1007.
23. C. Yang, R. Duraiswami, D. DeMenthon, and L. Davis, "Mean-shift analysis using quasi-newton methods," in Proc. IEEE Int. Conf. Image Processing, Barcelona, Spain, 2003, vol. 3, pp. 447-450.
24. L. Xu, "On convergence properties of the EM algorithm for Gaussian mixtures," Neural Comput., vol. 8, no. 1, pp. 129-151, Jan. 1996.
25. L. E. Ortiz and L. P. Kaelbling, "Accelerating EM:AN empirical study," in Proc. Uncertainty in Artificial Intell., Stockholm, Sweden, 1999, pp. 512-521.
26. R. Salakhutdinov and S. Roweis, "Adaptive overrelaxed bound optimization methods," in Proc. Int. Conf. Machine Learning, Washington, DC, 2003, pp. 664-671.
27. C. Shen, M. J. Brooks, and A. van den Hengel, "Fast global kernel density mode seeking with application to localisation and tracking," in Proc. IEEE Int. Conf. Computer Vision., Beijing, China, Oct. 2005, vol. 2, pp. 1516-1523.
28. D. Comaniciu, "An algorithm for data-driven bandwidth selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 2, pp. 281-288, Feb. 2003.
29. M. C. Jones, J. S. Marron, and S. J. Sheather, "A brief survey of bandwidth selection for density estimation," J. Amer. Statist. Assoc., vol. 91, no. 433, pp. 401-407, Mar. 1996.
30. P. Hall, M. C. Minnotte, and C. Zhang, "Bump hunting with non- Gaussian kernels," Ann. Statist., vol. 32, no. 5, pp. 2124-2141, 2004.
31. C. Yang, R. Duraiswami, N. Gumerov, and L. Davis, "Improved fast Gauss transform and efficient kernel density estimation," in Proc. IEEE Int. Conf. Comp. Vis., Nice, France, Oct. 2003, vol. 1, pp. 464-471.
32. P. Chaudhuri and J. S. Marron, "Scale space view of curve estimation," Ann. Statist., vol. 28, no. 2, pp. 408-428, 2000.
33. T. Lindeberg, Scale-Space Theory in Comp. Vis.. Norwell, MA: Kluwer, 1994.
34. A. Blake and A. Zisserman, Visual Reconstruction. Cambridge, MA: MIT Press, 1987.
35. D. Comaniciu, "Nonparametric information fusion for motion estimation," in Proc. IEEE Conf. Computer Vision Pattern Recognition Madison, WI, Jun. 2003, vol. 1, pp. 59-66.
36. M. Carreira-Perpinań and C. Williams, "On the number of modes of a Gaussian mixture," Tech. Rep. EDI-INF-RR-0159, School Informatics, Univ. Edinburgh, Edinburgh, U.K., Feb. 2003 [Online]. Available: http://www.inf.ed.ac.uk/publications/report/0159.html
37. S. Hettich and S. D. Bay, "The UCI KDD archive," Tech. Rep., Dept. Inf. Comput. Sci., University of California, Irvine, CA, 1999 [Online]. Available: http://kdd.ics.uci.edu
38. C. Shen and M. J. Brooks, "Adaptive over-relaxed mean shift," presented at the 8th Int. Symp. Signal Process. Applications, Sydney, Australia, Aug. 2005.
39. T. Liu and H. Chen, "Real-time tracking using trust-region methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 3, pp. 397-402, Mar. 2004.
40. D. C. Liu and J. Nocedal, "On the limited memory method for large scale optimization," Math. Programm. B, vol. 45, no. 3, pp. 503-528, 1989.
41. T. Kailath, "The divergence and Bhattacharyya distance measures in signal selection," IEEE Trans. Commun. Technol., vol. 15, no. 1, pp. 52-60, Feb. 1967.
42. S. Kullback and R. A. Leibler, "On information and sufficiency," Ann. Math. Statist., vol. 22, no. 1, pp. 79-86, 1951.
43. K. Okuma, A. Taleghani, N. de Freitas, J. J. Little, and D. G. Lowe, "A boosted particle filter: Multitarget detection and tracking," in Proc. Eur. Conf. Computer Vision, Prague, Czech Republic, 2004, vol. 1, pp. 28-39.
44. H. Wang and D. Suter, "False-peaks-avoiding mean shift method for unsupervised peak-valley sliding image segmentation," in Proc. 8th Digital Image Computing: Techniques and Applications, Sydney, Australia, 2003, pp. 581-590.
45. A. Kalai and S. Vempala, "Simulated annealing for convex optimization," Math. Oper. Res. [Online]. Available: http:// ttic.uchicago.edu/~kalai. In press.