Abstract
Plenty of multi-view video processing tasks such as video abstract, key-frame extraction and camera selection focus on presenting to audiences the most significant information in a certain period of time. Basically, the main idea of these techniques is to show audiences the video segments or frames that have the highest spatio-temporal significances. However, existing approaches are not enough to deal with these tasks with a general framework. In this paper, we develop a novel bottom-up algorithm called video clip growth that generates multi-view video abstract through an accurate frames adding process, which allows users to customize the length of the video summaries. This approach firstly uses an energy function to evaluate each frame’s importance from both time and space dimension. Then video clips and frames are gradually selected according to their energy rank, until reaching the target length. Besides, our algorithm can also extend to several multi-view video processing tasks. The experimental results on the Lobby and Office dataset have demonstrated the effectiveness of our algorithm.
This work was supported by Tianjin Philosophy and Social Science Planning Program under grant TJSR15-008, National Social Science Foundation under grant 15XMZ057.
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Pan, G., Qu, X., Lv, L., Guo, S., Sun, D. (2018). Video Clip Growth: A General Algorithm for Multi-view Video Summarization. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_11
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DOI: https://doi.org/10.1007/978-3-030-00764-5_11
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