Segmenting Motions of Different Types by Unsupervised Manifold Clustering

We propose a novel algorithm for segmenting multiple motions of different types from point correspondences in multiple affine or perspective views. Since point trajectories associated with different motions live in different manifolds, traditional approaches deal with only one manifold type: linear subspaces for affine views, and homographic, bilinear and trilinear varieties for two and three perspective views. As real motion sequences contain motions of different types, we cast motion segmentation as a problem of clustering manifolds of different types. Rather than explicitly modeling each manifold as a linear, bilinear or multilinear variety, we use nonlinear dimensionality reduction to learn a low-dimensional representation of the union of all manifolds. We show that for a union of separated manifolds, the LLE algorithm computes a matrix whose null space contains vectors giving the segmentation of the data. An analysis of the variance of these vectors allows us to distinguish them ...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Alvina Goh, René Vidal
• Keywords: Computer Vision, CVPR 2007, Motion Segmentation, Motion Segmentation Database, Motion Segmentation Methods, Nonlinear Manifolds, Separated Manifolds