Kernel methods for weakly supervised mean shift clustering

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 48-55

  Tuzel, Oncel ^a   Porikli, Fatih ^a   Meer, Peter ^b  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ACCURACY; CLUSTERING PROCESS; DATA ASSOCIATION; DATA POINTS; DISTANCE METRICS; HIGHER-DIMENSIONAL; INHERENT STRUCTURES; KERNEL FUNCTION; KERNEL METHODS; MEAN SHIFT; MEAN SHIFT ALGORITHM; MODE SEEKING; NUMBER OF CLUSTERS; PRIOR KNOWLEDGE; PROBLEM DOMAIN; SEMI-SUPERVISED; SYNTHETIC AND REAL DATA; UNSUPERVISED DATA;

CLUSTERING ALGORITHMS; COMPUTER VISION; PROBABILITY DISTRIBUTIONS; TARGET TRACKING; VECTOR QUANTIZATION;

CLUSTER ANALYSIS;

EID: 77953216511     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459204     Document Type: Conference Paper

References (25)

1. S. Basu, M. Bilenko, and R. Mooney. A probabilistic framework for semi-supervised clustering. In Proc. 10th Intl. Conf. on Knowledge Discovery and Data mining, 2004.
2. O. Boykov, Y. Veksler and R. Zabih. Fast approximate energy minimisation via graph cuts. In IEEE Trans. Pat. Anal. Mach. Intell., 2001.
3. M. Brand. Incremental singular value decomposition of uncertain data with missing values. In Proc. European Conf. on Comp. Vis., Copehagen, Denmark, pages 707-720, 2002.
4. M. A. Carreira-Perpinan. Gaussian mean-shift is an EM algorithm. IEEE Trans. Pat. Anal. Mach. Intell., 29(5):767-776, 2007.
5. O. Chapelle, B. Schlkopf, and A. Zien. Semi-Supervised Learning. MIT Press, 2006.
6. H. Chen and P. Meer. Robust fusion of uncertain information. IEEE Trans. Sys., Man, Cyber.-Part B, 35:578-586, 2005.
7. Y. Cheng. Mean shift, mode seeking, and clustering. IEEE Trans. Pat. Anal. Mach. Intell., 17:790-799, 1995.
8. R. Collins. Mean shift blob tracking through scale space. In Proc. IEEE Conf. on Comp. Vis. and Pat. Recog., Madison, WI, volume 2, pages 234-240, 2003.
9. D. Comaniciu. Variable bandwidth density-based fusion. In Proc. IEEE Conf. on Comp. Vis. and Pat. Recog., Madison, WI, volume 1, pages 56-66, 2003.
10. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pat. Anal. Mach. Intell., 24:603-619, 2002.
11. D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. In Proc. IEEE Conf. on Comp. Vis. and Pat. Recog., Hilton Head, SC, volume 1, pages 142-149, 2000.
12. N. Cristianini and J. Shawe-Taylor. Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, 2000.
13. M. Fashing and C. Tomasi. Mean shift is a bound optimization. IEEE Trans. Pat. Anal. Mach. Intell., 25:471-474, 2005.
14. K. Fukunaga and L. D. Hostetler. The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans. Information Theory, 21:32-40, 1975.
15. R. Ghani. Combining labeled and unlabeled data for multiclass text categorization. In Proc. 19th Intl. Conf. on Machine Learning, 2002.
16. L. Grady. Random walks for image segmentation. IEEE Trans. Pat. Anal. Mach. Intell., 28(11):1768-1783, 2006.
17. G. Hager, M. Dewan, and C. Stewart. Multiple kernel tracking with SSD. In Proc. IEEE Conf. on Comp. Vis. and Pat. Recog., Washington, DC, volume 1, pages 790-797, 2004.
18. B. Kulis, S. Basu, I. Dhillon, and R. Mooney. Semi-supervised graph clustering: a kernel approach. In Proc. 22nd Intl. Conf. on Machine Learning, 2005.
19. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Intl. J. of Comp. Vision, 60(2):91-110, 2004.
20. T. Sim, S. Baker, and M. Bsat. The cmu pose, illumination, and expression database. IEEE Trans. Pat. Anal. Mach. Intell., 25(12):1615-1618, 2003.
21. O. Tuzel, R. Subbarao, and P. Meer. Simultaneous multiple 3D motion estimation via mode finding on Lie groups. In Proc. 10th Intl. Conf. on Comp. Vis., Beijing, China, volume 1, pages 18-25, 2005.
22. A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In Proc. European Conf. on Comp. Vis., Marsille, France, pages 705-718, 2008.
23. K. Wafstaff and S. Rogers. Constrained k-means clustering with background knowledge. In Proc. 18th Intl. Conf. on Machine Learning, 2001.
24. J.Wang, B. Thiesson, Y. Xu, and M. Cohen. Image and video segmentation by anisotropic kernel mean shift. In Proc. European Conf. on Comp. Vis., Prague, Czech Republic, volume 2, pages 238-249, 2004.
25. B. Yan and C. Domeniconi. An adaptive kernel method for semi-supervised clustering. In Proc. European Conf. on Comp. Vis., Graz, Austria, 2006.