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CNN-Based Temporal Video Segmentation Using a Nonlinear Hyperbolic PDE-Based Multi-Scale Analysis

Author

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  • Tudor Barbu

    (Institute of Computer Science of Romanian Academy—Iasi Branch, 2 T. Codrescu Street, 700481 Iași, Romania
    The Academy of Romanian Scientists, 3 Ilfov Street, Sector 5, 050663 Bucharest, Romania)

Abstract

An automatic temporal video segmentation framework is introduced in this article. The proposed cut detection technique performs a high-level feature extraction on the video frames, by applying a multi-scale image analysis approach combining nonlinear partial differential equations (PDE) to convolutional neural networks (CNN). A nonlinear second-order hyperbolic PDE model is proposed and its well-posedness is then investigated rigorously here. Its weak and unique solution is determined numerically applying a finite difference method-based numerical approximation algorithm that quickly converges to it. A scale-space representation is then created using that iterative discretization scheme. A CNN-based feature extraction is performed at each scale and the feature vectors obtained at multiple scales are concatenated into a final frame descriptor. The feature vector distance values between any two successive frames are then determined and the video transitions are identified next, by applying an automatic clustering scheme on these values. The proposed PDE model, its mathematical investigation and discretization, and the multi-scale analysis based on it represent the major contributions of this work. Some temporal segmentation experiments and method comparisons that illustrate the effectiveness of the proposed framework are finally described in this research paper.

Suggested Citation

  • Tudor Barbu, 2023. "CNN-Based Temporal Video Segmentation Using a Nonlinear Hyperbolic PDE-Based Multi-Scale Analysis," Mathematics, MDPI, vol. 11(1), pages 1-12, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:245-:d:1023542
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    References listed on IDEAS

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    1. Tudor Barbu, 2020. "Feature Keypoint-Based Image Compression Technique Using a Well-Posed Nonlinear Fourth-Order PDE-Based Model," Mathematics, MDPI, vol. 8(6), pages 1-16, June.
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    Cited by:

    1. Qingliang Zhao & Xiaobin Feng & Liwen Zhang & Yiduo Wang, 2023. "Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising," Mathematics, MDPI, vol. 11(19), pages 1-16, October.

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