Matrix (20 papers)
C. David, V. Gu, F. Alexa, “Foreground/Background Segmentation with Learned Dictionary”, International Conference on Circuits, Systems and Signals, CSS 2009, pages 197-201, 2009.
C. David, V. Gui, “Automatic background subtraction in a sparse representation framework”, International Conference on Systems, Signals and Image Processing, IWSSIP 2013, page 63-,66, July 2013.
C. David, V. Gui, “Sparse coding and Gaussian modeling of coefficients average for background subtraction”, International Symposium on Image and Signal Processing and Analysis, ISPA 2013, pages 230-235, September 2013.
C. Zhao, X. Wang ,W. Cham, “Background Subtraction via Robust Dictionary Learning “, EURASIP Journal on Image and Video Processing, IVP 2011, January 2011.
R. Sivalingam, A. De Souza, V. Morellas, N. Papanikolopoulos, M. Bazakos, R. Miezianko, “Dictionary Learning for Robust Background Modeling”, IEEE International Conference on Robotics and Automation Shanghai International Conference Center, Shanghai, China, May 2011.
X. Huang, F. Wu, P. Huang, “Moving-object Detection Based on Sparse Representation and Dictionary Learning”, AASRI Conference on Computational Intelligence and Bioinformatics, Volume 1, pages 492-497, 2012.
C. Lu, J. Shi, J. Jia, “Online Robust Dictionary Learning”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, June 2013.
A. Stagliano, N. Noceti, A. Verri, F. Odone, “Background Modeling Through Dictionary Learning”, International Conference on Image Processing, ICIP 2013, Melbourne, Australia, September 2013.
A. Stagliano, N. Noceti, A. Verri, F. Odone, “Online space-variant background modeling with sparse coding”, IEEE Transactions on Image Processing, 2015.
M. Zhou, “Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling”, PhD thesis, Duke University, 2013.
N. Sang, T. Zhang, B. Li, X. Wu, “Dictionary-based background subtraction”, Journal of Huazhong University of Science and Technology, Volume 41, Issue 9, pages 28-31, September 2013.
H. Xiao, Y. Liu, S. Tan, J. Duan, M. Zhang, “A noisy videos background subtraction algorithm based on dictionary learning”, KSII Transactions on Internet and Information Systems, pages 1946-1963, 2014.
S. Zhang, S. Kasiviswanathan, P. Yuen, M. Harandi, “Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications”, AAAI Conference on Artificial Intelligence, AAAI 2015, January 2015.
Z. Ji, W. Wang, K. Lu, “Extract Foreground Objects Based on Sparse Model of Spatiotemporal Spectrum”, IEEE International conference on Image Processing, Melbourne, Australia, September 2013.
Z. Ji, W. Wang, K. Lu “Foreground Detection Utilizing Structured Sparse Model via l12 mixed norms”, IEEE International Conference on Systems, Man, and Cybernetics, October 2013.
J. Jiang, L. Jiang, N. Sang, “Spatial-Temporal Sparse Representation for Background Modeling”, International Conference on Image and Graphics, ICIG 2013, pages 656-660, July 2013.
D. Bao, F. Yang, Q. Jiang, S. Li, X. He, "Block RLS algorithm for surveillance video processing based on image sparse representation", Chinese Control And Decision Conference, CCDC 2017,pages 2195-2200, Chongqing, China, 2017.
S. Li, Z. Hu, M. Zhao, "Moving Object Detection Using Sparse Approximation and Sparse Coding Migration", KSII Transactions on Internet and Information Systems, Volume 14, No. 5, pages 2141-2155, 2020.
Q. Cao, Z. Wang, K. Long, "Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model", Hindawi Journal of Advanced Transportation, 14 pages, 2021.
G. Liang, N. Shi, R. Kontar, S. Fattahi, Personalized Dictionary Learning for Heterogeneous Datasets, Preprint, May 2023.
Tensor (2 papers)
S. Pei, L. Li, L. Ye, Y. Dong, "A tensor foreground-background separation algorithm based on dynamic dictionary update and active contour detection", IEEE Access, 2020.
Q. Luo, W. Li, M. Xiao, "Bayesian Dictionary Learning on Robust Tubal Transformed Tensor Factorization”, IEEE Transactions on Neural Networks and Learning Systems, 2023.