Incorporating constraints and prior knowledge into factorization algorithms – an application to 3d recovery

Authors:
Amit Gruber;Yair Weiss
Affiliations:
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel;School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Venue:
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Year:
2005

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Abstract

Matrix factorization is a fundamental building block in many computer vision and machine learning algorithms. In this work we focus on the problem of ”structure from motion” in which one wishes to recover the camera motion and the 3D coordinates of certain points given their 2D locations. This problem may be reduced to a low rank factorization problem. When all the 2D locations are known, singular value decomposition yields a least squares factorization of the measurements matrix. In realistic scenarios this assumption does not hold: some of the data is missing, the measurements have correlated noise, and the scene may contain multiple objects. Under these conditions, most existing factorization algorithms fail while human perception is relatively unchanged. In this work we present an EM algorithm for matrix factorization that takes advantage of prior information and imposes strict constraints on the resulting matrix factors. We present results on challenging sequences.