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Unequally Weighted Video Hashing for Copy Detection

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Advances in Multimedia Modeling (MMM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7732))

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Abstract

In this paper, an unequally weighted video hashing algorithm is presented, in which visual saliency is used to generate the video hash and weight different hash bits. The proposed video hash is fused by two hashes, which are the spatio-temporal hash (ST-Hash) generated according to the spatio-temporal video information and the visual hash (V-Hash) generated according to the visual saliency distribution. In order to emphasize the contribution of visual salient regions to video content, Weighted Error Rate (WER) is defined as an unequally weighted hash matching method to take the place of BER. The WER, unlike BER, gives hash bits unequal weights according to their corresponding visual saliency in hash matching. Experiments verify the robustness and discrimination of the proposed video hashing algorithm and show that the WER-based hash matching is helpful to achieve better precision rate and recall rate.

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Author information

Authors and Affiliations

  1. School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Jiande Sun

  2. School of Information Science and Engineering, Shandong University, Jinan, China

    Jiande Sun, Jing Wang, Hui Yuan, Xiaocui Liu & Ju Liu

  3. The Hisense State Key Laboratory of Digital-Media Technology, Qingdao, China

    Jiande Sun & Ju Liu

Authors
  1. Jiande Sun
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  2. Jing Wang
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  3. Hui Yuan
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  4. Xiaocui Liu
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  5. Ju Liu
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Editor information

Editors and Affiliations

  1. Microsoft Research Asia, 5 Danling Street, 100080, Beijing, China

    Shipeng Li  & Tao Mei  & 

  2. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, K1N 6N5, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. School of Computer and Information, Hefei University of Technology, Road Tunxi 193#, 230009, Hefei, Anhui, China

    Meng Wang  & Richang Hong  & 

  4. Department of Information Engineering and Computer Science, University of Trento, ommarive 14, 38100, Trento, Italy

    Nicu Sebe

  5. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore, Singapore

    Shuicheng Yan

  6. School of Computing, CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, Dublin 9, Ireland

    Cathal Gurrin

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© 2013 Springer-Verlag Berlin Heidelberg

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Sun, J., Wang, J., Yuan, H., Liu, X., Liu, J. (2013). Unequally Weighted Video Hashing for Copy Detection. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_50

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  • DOI: https://doi.org/10.1007/978-3-642-35725-1_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35724-4

  • Online ISBN: 978-3-642-35725-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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