Nonlinear Mean Shift for Clustering over Analytic Manifolds.
Raghav Subbarao and Peter Meer
Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
The mean shift algorithm is widely applied for nonparametric
clustering in Euclidean spaces. Recently, mean shift was
generalized for clustering on matrix Lie groups. We further extend
the algorithm to a more general class of nonlinear spaces, the set
of analytic manifolds. As examples, two specific classes of
frequently occurring parameter spaces, Grassmann manifolds and Lie
groups, are considered. When the algorithm proposed here is
restricted to matrix Lie groups the previously proposed method is
obtained. The algorithm is applied to a variety of robust motion
segmentation problems and multibody factorization. The motion
segmentation method is robust to outliers, does not require any
prior specification of the number of independent motions and
simultaneously estimates all the motions present.
2006 Computer Vision and Pattern Recognition Conference,
New York City, NY, June 2006, vol. I, 1168-1175.
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