Robust Statistical Estimation and Segmentation of Multiple Subspaces

Authors:
Allen Y. Yang;Shankar R. Rao;Yi Ma
Affiliations:
University of Illinois at Urbana-Champaign, USA;University of Illinois at Urbana-Champaign, USA;University of Illinois at Urbana-Champaign, USA
Venue:
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Year:
2006

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Abstract

We study the problem of estimating a mixed geometric model of multiple subspaces in the presence of a significant amount of outliers. The estimation of multiple subspaces is an important problem in computer vision, particularly for segmenting multiple motions in an image sequence. We first provide a comprehensive survey of robust statistical techniques in the literature, and identify three main approaches for detecting and rejecting outliers. Through a careful examination of these approaches, we propose and investigate three principled methods for robustly estimating mixed subspace models: random sample consensus, the influence function, and multivariate trimming. Using a benchmark synthetic experiment and a set of real image sequences, we conduct a thorough comparison of the three methods