A Simple Method for Subspace Estimation with Corrupted Columns
Paper in proceedings, 2016

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.

Optimization

Estimation

Closed-form solutions

Robustness

Computer vision

Convergence

Shape

Author

Viktor Larsson

Lund University

Claes Olsson

Lund University

Fredrik Kahl

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015; Santiago; Chile; 11 December 2015 through 18 December 2015

1550-5499 (ISSN)

Vol. 2016-February 841-849

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

Medical Image Processing

DOI

10.1109/ICCVW.2015.113

ISBN

9781467383905

More information

Latest update

3/2/2018 9