Manifold Constrained Low-Rank Decomposition

Chen Chen, Baochang Zhang, Alessio Del Bue, Vittorio Murino; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1800-1808

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 data contains significant occlusion, noise, illumination variation, and misalignment from rotation and/or viewpoint changing. In this paper, we propose a new framework that embeds manifold priors into LRD. To implement the framework, we design a multipliers alternating direction method which efficiently integrates the manifold constraints during the optimization process. This is due to the assumption that we can recast the problem as the projection over the manifold via an embedding method. The proposed approach is successfully used to calculate low ranks from faces, digits and window images, showing a consistent increase of performance when compared to the state of the art over a wide range of realistic misalignments and corruptions.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Chen_2017_ICCV,
author = {Chen, Chen and Zhang, Baochang and Del Bue, Alessio and Murino, Vittorio},
title = {Manifold Constrained Low-Rank Decomposition},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}