Probabilistic Measures for Motion Segmentation

The first contribution of this paper is a probabilistic ap-proach for measuring motion similarity for point sequences. While most motion segmentation algorithms are based on a rank-constraint on the space of affine motions, our method is based on spectral clustering of a probability measure for motion similarity which can be applied to any parametric model. The probabilistic framework allows for incorpora-tion of informative priors for the noise and camera motion. Similarly spatial and temporal priors can also be subsumed leading to useful segmentation techniques. Our second con-tribution is a tensor-decomposition technique enables us to infer motion affinity from higher dimensional representa-tions. Results are presented on real image sequences. 1