Robust Kronecker-decomposable component analysis for low-rank modeling

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Title: Robust Kronecker-decomposable component analysis for low-rank modeling
Authors: Bahri, M
Panagakis, Y
Zafeiriou, S
Item Type: Conference Paper
Abstract: 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.
Issue Date: 25-Dec-2017
Date of Acceptance: 22-Oct-2017
ISBN: 9781538610329
ISSN: 1550-5499
Publisher: IEEE
Start Page: 3372
End Page: 3381
Journal / Book Title: 2017 IEEE International Conference on Computer Vision (ICCV)
Volume: 2017
Copyright Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/N007743/1
Conference Name: IEEE International Conference on Computer Vision (ICCV)
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Publication Status: Published
Start Date: 2017-10-22
Finish Date: 2017-10-29
Conference Place: Venice, Italy
Online Publication Date: 2017-12-25
Appears in Collections:Faculty of Engineering

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