ROUTE
Robust Outlier Estimation for Low Rank Matrix Recovery
In practice, even very high-dimensional data are typically sampled from low-dimensional subspaces but with intrusion of outliers and/or noises. Recovering the underlying structure and the pollution from the observations is key to understanding and processing such data. Besides properly modeling the low-rank structure of subspace, how to handle the pollution is core regarding the performance of recovery. Often, the observed data is posed as a superimposition of the clean data and residual, while the residual can be roughly divided into two groups, including small dense noises and gross sparse outliers. Compared with small noises, outliers morelikely ruin the recovery, as they can be arbitrarily large. By considering the above, this work designs a method for recovering the low rank matrix with robust outlier estimation, termed as ROUTE, in a unified manner. This page provides a demo for ROUTE.
The demo program written in Matlab can be accessed from the following link in the form of a .RAR file. ROUTE demo code
This demo software is provided for research purposes only. A license must be obtained for any commercial applications.
Related Works
Xiaojie Guo and Zhouchen Lin, "Low Rank Matrix Recovery via Robust Outlier Estimation" IEEE Trans. on Image Processing, 2018 [PDF]
Xiaojie Guo and Zhouchen Lin, "ROUTE: Robust Outlier Estimation for Low Rank Matrix Recovery" IJCAI 17 [PDF]
Xiaojie Guo, "Online Robust Low Rank Matrix Recovery" IJCAI 2015
Xiaochun Cao, Liang Yang, and Xiaojie Guo* (*Corresponding Author), "Total Variation Regularized RPCA for Irregularly Moving Object Detection under Dynamic Background" , IEEE Trans. on Cybernetics, 2015.
Xiaojie Guo, Xinggang Wang, Liang Yang, Xiaochun Cao, and Yi Ma, "Robust Foreground Detection Using Smoothness and Arbitrariness Constraints" [Code], ECCV 2014
Xiaojie Guo, Xiaochun Cao, and Yi Ma, "Robust Separation of Reflection from Multiple Images" [Code (4 examples 93 M)][Code (1 example 11 M)] ，CVPR as ORAL, 2014 (SKLOIS Best Paper)
Xiaojie Guo and Xiaochun Cao，"Speeding Up Low Rank Matrix Recovery for Foreground Separation in Surveillance Videos"，ICME 2014
Xiaojie Guo, Siyuan Li, and Xiaochun Cao, "Motion Matters: A Novel Framework for Compressing Surveillance Videos", ACM MM 2013
Xiaojie Guo and Yi Ma, "Video Editing with Temporal, Spatial and Appearance Consistency", CVPR 2013
Yawen Xue*, Xiaojie Guo*, and Xiaochun Cao (* These authors contributed equally to this work.), "Motion Saliency Detection Using Low-rank and Sparse Decomposition" [Code and More Results], ICASSP 2012