Hybrid Linear Modeling via Local Best-Fit Flats

Authors:
Teng Zhang;Arthur Szlam;Yi Wang;Gilad Lerman
Affiliations:
School of Mathematics, University of Minnesota, Minneapolis, USA;Courant Institute of Mathematical Sciences, New York University, New York City, USA;School of Mathematics, University of Minnesota, Minneapolis, USA;School of Mathematics, University of Minnesota, Minneapolis, USA
Venue:
International Journal of Computer Vision
Year:
2012

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Abstract

We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e., subspaces approximating the data in local neighborhoods. The correct sizes of the local neighborhoods are determined automatically by the Jones' β 2 numbers (we prove under certain geometric conditions that our method finds the optimal local neighborhoods). The collection of subspaces is further processed by a greedy selection procedure or a spectral method to generate the final model. We discuss applications to tracking-based motion segmentation and clustering of faces under different illuminating conditions. We give extensive experimental evidence demonstrating the state of the art accuracy and speed of the suggested algorithms on these problems and also on synthetic hybrid linear data as well as the MNIST handwritten digits data; and we demonstrate how to use our algorithms for fast determination of the number of affine subspaces.