University of Oulu

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

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Author: Bahri, Mehdi1; Panagakis, Yannis1,2; Zafeiriou, Stefanos1,3
Organizations: 1Imperial College London, UK
2Middlesex University London, UK
3University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
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.

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ISBN: 978-1-5386-1032-9
ISBN Print: 978-1-5386-1033-6
Pages: 3372 - 3381
DOI: 10.1109/ICCV.2017.363
Host publication: 2017 IEEE International Conference on Computer Vision (ICCV)
Conference: 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
Funding: 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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