relation: https://khub.utp.edu.my/scholars/10286/
title: SVD-Based Tensor-Completion Technique for Background Initialization
creator: Kajo, I.
creator: Kamel, N.
creator: Ruichek, Y.
creator: Malik, A.S.
description: Extracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular-value decomposition (SVD) of spatiotemporally extracted slices from the video tensor. The proposed method is referred to as spatiotemporal slice-based SVD (SS-SVD). To determine the SVD components that best model the background, a depth analysis of the computation of the left/right singular vectors and singular values is performed, and the relationship with tensor-tube fibers is determined. The analysis proves that a rank-1 matrix extracted from the first left and right singular vectors and singular value represents an efficient model of the scene background. The performance of the proposed SS-SVD method is evaluated using 93 complex video sequences of different challenges, and the method is compared with state-of-the-art tensor/matrix completion-based methods, statistical-based methods, search-based methods, and labeling-based methods. The results not only show better performance over most of the tested challenges, but also demonstrate the capability of the proposed technique to solve the background-initialization problem in a less computational time and with fewer frames. © 1992-2012 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Article
type: PeerReviewed
identifier:   Kajo, I. and Kamel, N. and Ruichek, Y. and Malik, A.S.  (2018) SVD-Based Tensor-Completion Technique for Background Initialization.  IEEE Transactions on Image Processing, 27 (6).  pp. 3114-3126.  ISSN 10577149     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044044731&doi=10.1109%2fTIP.2018.2817045&partnerID=40&md5=56caf57c232f6ddd50c175199cdc9746
relation: 10.1109/TIP.2018.2817045
identifier: 10.1109/TIP.2018.2817045