NDLI: SBMI-LTD: stationary background model initialization based on low-rank tensor decomposition

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SBMI-LTD: stationary background model initialization based on low-rank tensor decomposition

Content Provider	ACM Digital Library
Author	Jung, Soon Ki Javed, Sajid Bouwmans, Thierry
Abstract	Initialization of background model also known as foreground-free image against outliers or noise is a very important task for various computer vision applications. Tensor deomposition using Higher Order Robust Principal Component Analysis has been shown to be a very efficient framework for exact recovery of low-rank (corresponds to the background model) component. Recent study shows that tensor decomposition based on online optimization into low- rank and sparse component addressed the limitations of memory and computational issues as compared to the earlier approaches. However, it is based on the iterative optimization of nuclear norm which is not always robust when the large entries of an input observation tensor are corrupted against outliers. Therefore, the task of background modeling shows a weak performance in the presence of an increasing number of outliers. To address this issue, this paper presents an extension of an online tensor decomposition into low-rank and sparse components using a maximum norm constraint. Since, maximum norm regularizer is more robust than nuclear norm against large number of outliers, therefore the proposed extended tensor based decomposition framework with maximum norm provides an accurate estimation of background scene. Experimental evaluations on synthetic data as well as real dataset such as Scene Background Modeling Initialization (SBMI) show encouraging performance for the task of background modeling as compared to the state of the art approaches.
Starting Page	195
Ending Page	200
Page Count	6
File Format	PDF
ISBN	9781450344869
DOI	10.1145/3019612.3019687
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-04-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Tensor decomposition Robust principal component analysis Background initialization Background modeling
Content Type	Text
Resource Type	Article

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