Abstract
One of the first and most important steps in content-based video retrieval is the cut detection. Its effectiveness has a major impact towards subsequent high-level applications such as video summarization. In this paper, a robust video cut detector (VCD) based on different theorems related to the singular value decomposition (SVD) is proposed. In our contribution, the Frobenius norm is performed to estimate the appropriate reduced features from the SVD of concatenated block based histograms (CBBH). After that, according to each segment, each frame will be mapped into \(\tilde{k}\)-dimensional vector in the singular space. The classification of continuity values is achieved using an adjusted thresholding technique. Experimental results show the efficiency of our detector, which outperforms recent related methods in detecting the hard cut transitions.
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Bendraou, Y., Essannouni, F., Salam, A., Aboutajdine, D. (2016). Video Cut Detector via Adaptive Features using the Frobenius Norm. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_37
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DOI: https://doi.org/10.1007/978-3-319-50832-0_37
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