@article{scholars10286,
             doi = {10.1109/TIP.2018.2817045},
            note = {cited By 29},
          volume = {27},
          number = {6},
           title = {SVD-Based Tensor-Completion Technique for Background Initialization},
            year = {2018},
           pages = {3114--3126},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Transactions on Image Processing},
            issn = {10577149},
          author = {Kajo, I. and Kamel, N. and Ruichek, Y. and Malik, A. S.},
        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. {\^A}{\copyright} 1992-2012 IEEE.},
        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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044044731&doi=10.1109\%2fTIP.2018.2817045&partnerID=40&md5=56caf57c232f6ddd50c175199cdc9746}
}