Multiple Subspaces Separation in Case of Camera Motion

We explore the problem of subspace clustering. Given a set of data samples approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into respective subspaces, and also remove possible outliers. We propose an Approximated Robust PCA Clustering (ARPCAC) method that involves extracting the point trajectories only induced by object motion, from the pool of all motions induced by objects and camera motion, and then projecting them onto a 5-dimensional space, using PowerFactorization. Our algorithm can be used to segment multiple motions in video and furthermore, is extended to the problem of face clustering. Conducted experiments demonstrate state-of-the-art performance