Quick shift and kernel methods for mode seeking

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5305 LNCS, Issue PART 4, 2008, Pages 705-718

  Vedaldi, Andrea ^a   Soatto, Stefano ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE SEGMENTATION;

AUTOMATIC DISCOVERY; CLUSTERING DATA; DATA POINTS; KERNEL MATRICES; KERNEL METHODS; MEAN SHIFT; MODE SEEKING; NON-EUCLIDEAN SPACES;

CLUSTERING ALGORITHMS;

EID: 56749131266     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88693-8_52     Document Type: Conference Paper

References (25)

1. Bach, F.R., Jordan, M.I.: Kernel independent componet analysis. Journal of Machine Learninig Research 3(1) (2002)
2. Carreira-Perpiñán, M.: Fast nonparametric clustering with gaussian blurring meanshift. In: Proc. ICML (2006)
3. Cheng, Y.: Mean shift, mode seeking, and clustering. PAMI 17(8) (1995)
4. Choi, H., Choi, S.: Robust kernel isomap. Pattern Recognition (2006)
5. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. PAMI 24(5) (2002)
6. Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: Proc. ICCV (2001)
7. Csurka, C., Dance, C.R., Dan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proc. ECCV (2004)
8. Fine, S., Scheinberg, K.: Efficient SVM training using low-rank kernel representations. Journal of Machine Learninig Research (2001)
9. Fukunaga, K., Hostler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. 21(1) (1975)
10. Grauman, K., Darrell, T.: Unsupervised learning of categories from sets of partially matching image features. In: Proc. CVPR (2006)
11. Hein, M., Bousquet, O.: Hilbertian metrics and positive definite kernels on probability measures. In: Proc. AISTAT (2005)
12. Knuth, D.: The Art of Computer Programming: Seminumerical Algorithms, 3rd edn., vol. 2 (1998)
13. Koontz, W.L.G., Narendra, P., Fukunaga, K.: A graph-theoretic approach to non-parametric cluster analyisis. IEEE Trans. on Computers c-25(9) (1976)
14. Lowe, D.: Implementation of the scale invariant feature transform (2007), http://www.cs.ubc.ca/~lowe/keypoints/
15. Mika, S., Rätsch, C., Weston, J.. Schölkopf, B., Müller, K.-R.: Fisher discriminant analysis with kernels. In: Proc. IEEE Neural Networks for Signal Processing Workshop (1999)
16. Paris, S., Durand, F.: A topological approach to hierarchical segmentation using mean shift. In: Proc. CVPR (2007)
17. Sain, S.R.: Multivariate locally adaptive density estimation. Comp. Stat. and Data Analysis, 39 (2002)
18. Schölkopf, B.: The kernel trick for distances. In: Proc. NIPS (2001)
19. Schölkopf, B., Smola, A.J.: Learning with Kernels, MIT Press, Cambridge (2002)
20. Sheikh, Y.A., Khan, E.A., Kanade, T.: Mode-seeking by medoidshifts. In: Proc. CVPR (2007)
21. Subbarao, R., Meer, P.: Nonlinear mean shift for clustering over analytic manifolds. In: Proc. CVPR (2006)
22. Sundaramoorthy, G., Yezzi, A., Mennucci, A.: Sobolev active contours. Int. J. Comput. Vision 73(3) (2007)
23. Yang, C., Duraiswami, R., Gumerov, N.A., Davis, L.: Improved fast Gauss transform and efficient kernel density estimation. In: Proc. TCCV (2003)
24. Yuan, X., Li, S.Z.: Half quadric analysis for mean shift: with extension to a sequential data mode-seeking method. In: Proc. CVPR (2007)
25. Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study IJCV (2006)