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An image-based near-duplicate video retrieval and localization using improved Edit distance

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Abstract

The rapid development of social network in recent years has spurred enormous growth of near-duplicate videos. The existence of huge volumes of near-duplicates shows a rising demand on effective near-duplicate video retrieval technique in copyright violation and search result reranking. In this paper, we propose an image-based algorithm using improved Edit distance for near-duplicate video retrieval and localization. By regarding video sequences as strings, Edit distance is used and extended to retrieve and localize near-duplicate videos. Firstly, bag-of-words (BOW) model is utilized to measure the frame similarities, which is robust to spatial transformations. Then, non-near-duplicate videos are filtered out by computing the proposed relative Edit distance similarity (REDS). Next, a detect-and-refine-strategy-based dynamic programming algorithm is proposed to generate the path matrix, which can be used to aggregate scores for video similarity measure and localize the similar parts. Experiments on CC_WEB_VIDEO and TREC CBCD 2011 datasets demonstrated the effectiveness and robustness of the proposed method in retrieval and localization tasks.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61175096). The authors would like to thank the anonymous editor and reviewers who gave valuable suggestion that have helped to improve the quality of the manuscript.

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Hao Liu, Qingjie Zhao, Hao Wang, Peng Lv & Yanming Chen

Authors
  1. Hao Liu
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  2. Qingjie Zhao
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  3. Hao Wang
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  4. Peng Lv
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  5. Yanming Chen
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Correspondence to Hao Liu.

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Liu, H., Zhao, Q., Wang, H. et al. An image-based near-duplicate video retrieval and localization using improved Edit distance. Multimed Tools Appl 76, 24435–24456 (2017). https://doi.org/10.1007/s11042-016-4176-6

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  • DOI: https://doi.org/10.1007/s11042-016-4176-6

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