Abstract
The mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multi-body motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates and influence clustering accuracy. We propose to use both subspaces and observations to improve performance. Furthermore, while these mean shift methods use fixed metrics for computing distances, we prefer an adaptive distance measure. The insight is, we can use temporary modes in a mode seeking process to improve this measure and obtain better performance. In this paper, an adaptive mode seeking algorithm is proposed for clustering linear subspaces. By experiments, the proposed algorithm compares favorably to the state-of-the-art algorithm in terms of clustering accuracy.
Chapter PDF
Similar content being viewed by others
References
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)
Subbarao, R., Meer, P.: Nonlinear mean shift over riemannian manifolds. International Journal of Computer Vision 84(1), 1–20 (2009)
Çetingül, H., Vidal, R.: Intrinsic mean shift for clustering on stiefel and grassmann manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1896–1902 (2009)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 790–799 (1995)
Vidal, R.: A tutorial on subspace clustering. IEEE Signal Processing Magazine (to appear)
Ho, J., Yang, M.H., Lim, J., Leem, K.C., Kriegman, D.: Clustering appearances of objects under varying illumination conditions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 11–18 (2003)
Turaga, P., Veeraraghavan, A., Chellappa, R.: Statistical analysis on stiefel and grassmann manifolds with applications in computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21, 32–40 (1975)
Sheikh, Y., Khan, E., Kanade, T.: Mode-seeking by medoidshifts. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Absil, P.A., Mahony, R., Sepulchre, R.: Optimization algorithms on matrix manifolds. Princeton University Press (2007)
Cook, R.D., Weisberg, S.: Residuals and Influence in Regression. Chapman and Hall, New York (1982)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 409–415 (2003)
Ling, H., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 286–299 (2007)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Generative models for recognition under variable pose and illumination. In: IEEE Computer Society Conference on Automatic Face and Gesture Recognition, pp. 277–284 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pan, G., Shang, L., Schnieders, D., Wong, KY.K. (2012). Mode Seeking with an Adaptive Distance Measure. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33885-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-33885-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33884-7
Online ISBN: 978-3-642-33885-4
eBook Packages: Computer ScienceComputer Science (R0)