Abstract
With the development of network, there exists many near-duplicate videos online shared by individuals. These ones cause problems such as copyright infringement and search result redundancy. To solve the issues, this paper proposes a filter-and-refine framework for near-duplicate video retrieval and localization. By regarding video sequences as strings, Edit distance is used and improved in the approach. Firstly, bag-of-words (BOW) model is utilized to measure the similarities between frames. Then, non-near-duplicate videos are filtered out by computing the proposed relative Edit distance similarity (REDS). Next, a dynamic programming strategy is proposed to rank the remained videos and localize the similar segments. Experiments demonstrate the effectiveness and robustness of the method in retrieval and localization.
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Acknowledgement
This work is supported by the National Science Foundation of China (No. 61175096) and Specialized Fund for Joint Building Program of Bei**g Municipal Education Commission.
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Liu, H., Zhao, Q., Wang, H., Zhang, C. (2016). Near-Duplicate Web Video Retrieval and Localization Using Improved Edit Distance. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_12
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DOI: https://doi.org/10.1007/978-3-319-45814-4_12
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