Quaternion-based dynamic mode decomposition for background modeling in color videos
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Abstract (arXiv)
Scene Background Initialization (SBI) is one of the challenging problems in computer vision. Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. However, this method needs to convert the color image into the grayscale image for processing, which leads to the neglect of the coupling information between the three channels of the color image. In this study, we propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis, so as to completely preserve the inherent color structure of the color image and the color video. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues. The results on the publicly available benchmark datasets prove that our Q-DMD outperforms the exact DMD method, and experiment results also demonstrate that the performance of our approach is comparable to that of the state-of-the-art ones.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 54 references that are in the map. First posted on arXiv 28 Dec 2021.
Builds on
8 of 54 references.
References · 8
- 2018Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluationcited by 26
- 2021Deep Learning Enhanced Dynamic Mode Decompositioncited by 19
- 2015Compressed Dynamic Mode Decomposition for Background Modelingcited by 17
- 2019Color image recovery using low-rank quaternion matrix completion algorithmcited by 14
- 2015Towards Benchmarking Scene Background Initializationcited by 13
- 2015Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Datasetcited by 13
- 2020Quaternion-based bilinear factor matrix norm minimization for color image inpaintingcited by 12
- 2018Unsupervised Deep Context Prediction for Background Foreground Separationcited by 7
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2024Non-Negative Reduced Biquaternion Matrix Factorization with Applications in Color Face Recognitioncited by 1
- 2023L2,1-Norm Regularized Quaternion Matrix Completion Using Sparse Representation and Quaternion QR Decompositioncited by 0
- 2024Real-Time Motion Detection Using Dynamic Mode Decompositioncited by 0
- 2024Non-Negative Reduced Biquaternion Matrix Factorization with Applications in Color Face Recognition10 Aug 2024
- 2024Real-Time Motion Detection Using Dynamic Mode Decomposition8 May 2024
- 2023L2,1-Norm Regularized Quaternion Matrix Completion Using Sparse Representation and Quaternion QR Decomposition7 Sep 2023