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A Simple Method for Subspace Estimation with Corrupted Columns

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Author

Summary, in English

This paper presents a simple and effective way of solving the robust subspace estimation problem where the corruptions are column-wise. The method we present can handle a large class of robust loss functions and is simple to implement. It is based on Iteratively Reweighted Least Squares (IRLS) and works in an iterative manner by solving a weighted least-squares rank-constrained problem in every iteration. By considering the special case of column-wise loss functions, we show that each such surrogate problem admits a closed form solution. Unlike many other approaches to subspace estimation, we make no relaxation of the low-rank constraint and our method is guaranteed to produce a subspace estimate with the correct dimension. Subspace estimation is a core problem for several applications in computer vision. We empirically demonstrate the performance of our method and compare it to several other techniques for subspace estimation. Experimental results are given for both synthetic and real image data including the following applications: linear shape basis estimation, plane fitting and non-rigid structure from motion.

Publishing year

2016-02-11

Language

English

Pages

841-849

Publication/Series

Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015

Volume

2016-February

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computer Vision and Robotics (Autonomous Systems)

Keywords

  • Closed-form solutions
  • Computer vision
  • Convergence
  • Estimation
  • Optimization
  • Robustness
  • Shape

Conference name

15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015

Conference date

2015-12-11 - 2015-12-18

Conference place

Santiago, Chile

Status

Published

ISBN/ISSN/Other

  • ISBN: 9781467383905