Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC

In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas, including computer vision with applications in affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically t...

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Bibliographic Details
Published in:2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5820 - 5829
Main Authors: Oskarsson, Magnus, Batstone, Kenneth, Astrom, Kalle
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2016
Subjects:
Computer vision
Computers
Indexes
Manifolds
Noise measurement
Optimization
Robustness
ISSN:1063-6919
Online Access:Get full text
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Summary:In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas, including computer vision with applications in affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives a robust and fast system, with state-of-the-art performance.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.627