Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC

Summary, in English

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 e.g. 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 an robust and fast system, with state-of-the-art performance.

• Author: Magnus Oskarsson; Kenneth Batstone; Kalle Åström
• Department/s: Mathematics (Faculty of Engineering); Mathematical Imaging Group; Centre for Mathematical Sciences; ELLIIT: the Linköping-Lund initiative on IT and mobile communication; eSSENCE: The e-Science Collaboration
• Publishing year: 2016-06-01
• Language: English
• Pages: 5820-5829
• Publication/Series: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), proceedings of
• Links: http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Oskarsson_Trust_No_One_CVPR_2016_paper.html
• Document type: Conference paper
• Publisher: Computer Vision Foundation
• Topic: Mathematics; Computer Vision and Robotics (Autonomous Systems)
• Conference name: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• Conference date: 2016-06-27 - 2016-06-30
• Conference place: Seattle, United States
• Status: Published
• Project: Semantic Mapping and Visual Navigation for Smart Robots
• Research group: Mathematical Imaging Group