M. Bahri, Y. Panagakis and S. Zafeiriou, "Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 3372-3381. doi: 10.1109/ICCV.2017.363
Robust kronecker-decomposable component analysis for low-rank modeling
|Author:||Bahri, Mehdi1; Panagakis, Yannis1,2; Zafeiriou, Stefanos1,3|
1Imperial College London, UK
2Middlesex University London, UK
3University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019100330983
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-10-03
Dictionary learning and component analysis are part of one of the most well-studied and active research fields, at the intersection of signal and image processing, computer vision, and statistical machine learning. In dictionary learning, the current methods of choice are arguably K-SVD and its variants, which learn a dictionary (i.e., a decomposition) for sparse coding via Singular Value Decomposition. In robust component analysis, leading methods derive from Principal Component Pursuit (PCP), which recovers a low-rank matrix from sparse corruptions of unknown magnitude and support. However, K-SVD is sensitive to the presence of noise and outliers in the training set. Additionally, PCP does not provide a dictionary that respects the structure of the data (e.g., images), and requires expensive SVD computations when solved by convex relaxation. In this paper, we introduce a new robust decomposition of images by combining ideas from sparse dictionary learning and PCP. We propose a novel Kronecker-decomposable component analysis which is robust to gross corruption, can be used for low-rank modeling, and leverages separability to solve significantly smaller problems. We design an efficient learning algorithm by drawing links with a restricted form of tensor factorization. The effectiveness of the proposed approach is demonstrated on real-world applications, namely background subtraction and image denoising, by performing a thorough comparison with the current state of the art.
|Pages:||3372 - 3381|
2017 IEEE International Conference on Computer Vision (ICCV)
IEEE International Conference on Computer Vision
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
The work of Y. Panagakis has been partially supported by the European Community Horizon 2020 [H2020/2014-2020] under Grant Agreement No. 645094 (SEWA). S. Zafeiriou was partially funded by EPSRC Project EP/N007743/1 (FACER2VM).
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