A Simple Method for Subspace Estimation with Corrupted Columns
Paper i proceeding, 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

Författare

Viktor Larsson

Lunds universitet

Claes Olsson

Lunds universitet

Fredrik Kahl

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

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

Styrkeområden

Informations- och kommunikationsteknik

Livsvetenskaper och teknik

Ämneskategorier

Elektroteknik och elektronik

Medicinsk bildbehandling

DOI

10.1109/ICCVW.2015.113

ISBN

9781467383905

Mer information

Senast uppdaterat

2018-03-02