Abstract
Retake detection has been a challenging problem in rushes video summarization. Previous approaches represent video segments as a sequence of labels then find retakes by grouping similar sub-sequences using some sequence alignment algorithm. However, these kinds of representation usually lead to unsatisfactory results because it is difficult to know the number of labels needed for a video. In our method, instead of quantizing each video segment into a label, we formulate it as a binary classification problem between pairs of segments. We use this information as the input for the Smith-Waterman algorithm to detect and group similar video sub-sequences to find retakes. Our experiments evaluated on the standard benchmark dataset of TRECVID BBC Rushes 2007 show the effectiveness of the proposed method.
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Tran, QV., Le, DD., Duong, D.A., Satoh, S. (2013). A Classification-Based Approach for Retake and Scene Detection in Rushes Video. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_75
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DOI: https://doi.org/10.1007/978-3-642-42051-1_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
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