Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6959453 | Signal Processing | 2015 | 27 Pages |
Abstract
Robust visual analysis plays an important role in a great variety of computer vision tasks, such as motion segmentation, pose and face analysis. One of the promising real-world applications is to recover the clean data representation from the corrupted data points for subspace segmentation. Recently, low-rank based methods have gained considerable popularity in solving this problem, such as Low-Rank Representation (LRR) and Fixed-Rank Representation (FRR). They both learn a low-rank data matrix and a sparse error matrix. Each new data representation is learnt using the whole dictionary covering all data points. However, they neglect a common fact that each point can be represented by a linear combination of only a few other points w.r.t. a given dictionary, which has been shown in sparse learning. Motivated by this, we explicitly impose the sparsity constraint on the learnt low-rank representation. To be more efficient, we adopt a fixed-rank scheme by minimizing the Frobenius norm of the new representation. Hence, in this paper we propose a novel Sparse Fixed-Rank Representation (SFRR) approach for robust visual analysis. Specifically, we model the corruptions by enforcing a sparse regularizer. This way, we can obtain a new data representation with both low-rankness and sparseness robustly. Furthermore, we present a generalized alternating direction method (ADM) to optimize the objective function. Extensive experiments on both synthetic and real-world databases have suggested the effectiveness and the robustness of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Ping Li, Jiajun Bu, Bin Xu, Zhanying He, Chun Chen, Deng Cai,