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.
Cite
Text
Chen et al. "Manifold Constrained Low-Rank Decomposition." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.213Markdown
[Chen et al. "Manifold Constrained Low-Rank Decomposition." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/chen2017iccvw-manifold/) doi:10.1109/ICCVW.2017.213BibTeX
@inproceedings{chen2017iccvw-manifold,
title = {{Manifold Constrained Low-Rank Decomposition}},
author = {Chen, Chen and Zhang, Baochang and Del Bue, Alessio and Murino, Vittorio},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {1800-1808},
doi = {10.1109/ICCVW.2017.213},
url = {https://mlanthology.org/iccvw/2017/chen2017iccvw-manifold/}
}