SVD-Based Tensor-Completion Technique for Background Initialization

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.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Computational complexity; Feature extraction; Image converters; Image reconstruction; Matrix converters; Tensile stress; Tensors, Background initialization; Matrix decomposition; Spatio-temporal slices; Spatiotemporal phenomena; Tensor completion, Singular value decomposition
Departments / MOR / COE: Research Institutes > Institute for Health Analytics
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 01 Aug 2018 03:12
Last Modified: 16 Nov 2018 08:27
URI: http://scholars.utp.edu.my/id/eprint/21548

Actions (login required)

View Item
View Item