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Duplicate video detection for large-scale multimedia

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Abstract

Since rapid growth of IT technologies, the use of multimedia data such as image and videos are explosively increasing. It is an important aspect of not only for users but also researchers. Duplicate images and videos are rapidly increasing and it causes difficulties in retrieval and management as well. It also causes copyright problems. In this paper, we discus prior duplicate video detection techniques and overcome previous research problems using block histogram and dynamic matching approach duplicate video detection method. We improved excessive abstract of previous block mean-value based feature extraction method to be robust in various video transformations. Also, we created feature vector of timely histogram by unit of blocks to reflect video features. We proposed dynamic matching algorithm to match videos which is suitable for large-scale video data. To evaluate our proposal, we used VIREO video datasets which is provided by Hong Kong City University and Carnegie Mellon University and MUSCLE-VCD-2007 dataset which is provided by INRIA. Our method showed 90 % of accuracy on duplicate video detection. Our proposed method showed robustness especially in various video transformations. Also, we tested video clustering test to prove our method and dynamic matching method showed 5 times fast compare to existing method which is suitable for real-time and large-scale video detection process.

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Authors and Affiliations

  1. Department of Computer Science, Yonsei University, 50 Sungsan Road, Seodaemun, Seoul, South Korea

    Woogyoung Jun & Yillbyung Lee

  2. Department of Computer Engineering, Chungbuk National University, 52 Naesudong Road, Heungduk, Cheongju, South Korea

    Byoung-Min Jun

Authors
  1. Woogyoung Jun
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  2. Yillbyung Lee
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  3. Byoung-Min Jun
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Correspondence to Byoung-Min Jun.

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Jun, W., Lee, Y. & Jun, BM. Duplicate video detection for large-scale multimedia. Multimed Tools Appl 75, 15665–15678 (2016). https://doi.org/10.1007/s11042-015-2724-0

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  • DOI: https://doi.org/10.1007/s11042-015-2724-0

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