Abstract

In this paper, we propose a robust motion segmentation method using the techniques of matrix factorization and subspace sepa- ration. We first show that the shape interaction matrix can be derived us- ing QR decomposition rather than Singular Value Decomposition(SVD) which also leads to a simple proof of the shape subspace separation theo- rem. Using the shape interaction matrix, we solve the motion segmenta- tion problems by the spectral clustering techniques. We exploit multi-way Min-Max cut clustering method and provide a novel approach for cluster membership assignment. We further show that we can combine a cluster refinement method based on subspace separation with the graph clus- tering method to improve its robustness in the presence of noise. The proposed method yields very good performance for both synthetic and real image sequences.