TY - GEN
T1 - SBMI-LTD
T2 - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
AU - Javed, Sajid
AU - Bouwmans, Thierry
AU - Jung, Soon Ki
N1 - Publisher Copyright:
Copyright 2017 ACM.
PY - 2017/4/3
Y1 - 2017/4/3
N2 - 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 lowrank 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.
AB - 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 lowrank 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.
KW - Background initialization
KW - Background modeling
KW - Robust principal component analysis
KW - Tensor decomposition
UR - http://www.scopus.com/inward/record.url?scp=85015144142&partnerID=8YFLogxK
U2 - 10.1145/3019612.3019687
DO - 10.1145/3019612.3019687
M3 - Conference contribution
AN - SCOPUS:85015144142
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 195
EP - 200
BT - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
Y2 - 4 April 2017 through 6 April 2017
ER -