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An Efficient Hierarchical Near-Duplicate Video Detection Algorithm Based on Deep Semantic Features

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

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Abstract

With the rapid development of the Internet and multimedia technology, the amount of multimedia data on the Internet is escalating exponentially, which has attracted much research attention in the field of Near-Duplicate Video Detection (NDVD). Motivated by the excellent performance of Convolutional Neural Networks (CNNs) in image classification, we bring the powerful discrimination ability of the CNN model to the NDVD system and propose a hierarchical detection method based on the derived deep semantic features from the CNN models. The original CNN features are firstly extracted from the video frames, and then a semantic descriptor and a labels descriptor are obtained respectively based on the basic content unit. Finally, a hierarchical matching scheme is proposed to promote fast near-duplicate video detection. The proposed approach has been tested on the widely used CC_WEB_VIDEO dataset, and has achieved state-of-the-art results with the mean Average Precision (mAP) of 0.977.

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Notes

  1. 1.

    https://www.omnicoreagency.com/youtube-statistics/.

  2. 2.

    https://github.com/BVLC/caffe/wiki/Model-Zoo.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Siying Liang & Ping Wang

Authors
  1. Siying Liang
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  2. Ping Wang
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Corresponding author

Correspondence to Ping Wang .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Liang, S., Wang, P. (2020). An Efficient Hierarchical Near-Duplicate Video Detection Algorithm Based on Deep Semantic Features. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_61

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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