Title

Manifold Constrained Low-Rank Decomposition

Abstract

Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling. However, it is a very challenging problem when the image data contains significant occlusion, noise, illumination variation, and misalignment from rotation or viewpoint changes. We leverage the specific structure of data in order to improve the performance of LRD when the data are not ideal. To this end, we propose a new framework that embeds manifold priors into LRD. To implement the framework, we design an alternating direction method of multipliers (ADMM) method which efficiently integrates the manifold constraints during the optimization process. The proposed approach is successfully used to calculate low-rank models from face images, hand-written digits and planar surface images. The results show a consistent increase of performance when compared to the state-of-the-art over a wide range of realistic image misalignments and corruptions.

Publication Date

7-1-2017

Publication Title

Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017

Volume

2018-January

Number of Pages

1800-1808

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCVW.2017.213

Socpus ID

85046295494 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046295494

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