Spectral clustering for robust motion segmentation

Abstract

In this paper, we propose a robust motion segmentation method using the techniques of matrix factorization and subspace separation. We first show that the shape interaction matrix can be derived using QR decomposition rather than Singular Value Decomposition(SVD) which also leads to a simple proof of the shape subspace separation theorem. Using the shape interaction matrix, we solve the motion segmentation 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 clustering method to improve its robustness in the presence of noise. The proposed method yields very good performance for both synthetic and real image sequences. © Springer-Verlag Berlin Heidelberg 2004.