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Finding Near-Duplicate Videos in Large-Scale Collections

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Video Verification in the Fake News Era

Abstract

This chapter discusses the problem of Near-Duplicate Video Retrieval (NDVR). The main objective of a typical NDVR approach is: given a query video, retrieve all near-duplicate videos in a video repository and rank them based on their similarity to the query. Several approaches have been introduced in the literature, which can be roughly classified in three categories based on the level of video matching, i.e., (i) video-level, (ii) frame-level, and (iii) filter-and-refine matching. Two methods based on video-level matching are presented in this chapter. The first is an unsupervised scheme that relies on a modified Bag-of-Words (BoW) video representation. The second is a s upervised method based on Deep Metric Learning (DML). For the development of both methods, features are extracted from the intermediate layers of Convolutional Neural Networks and leveraged as frame descriptors, since they offer a compact and informative image representation, and lead to increased system efficiency. Extensive evaluation has been conducted on publicly available benchmark datasets, and the presented methods are compared with state-of-the-art approaches, achieving the best results in all evaluation setups.

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Notes

  1. 1.

    https://blogs.wsj.com/metropolis/2011/04/28/weather-journal-clouds-gathered-but-no-tornado-damage/ (accessed on March 2019).

  2. 2.

    https://images.google.com/ (accessed on March 2019).

  3. 3.

    https://www.tineye.com/ (accessed on March 2019).

  4. 4.

    https://www.youtube.com/yt/about/press/ (accessed on March 2019).

  5. 5.

    https://github.com/BVLC/caffe/wiki/Model-Zoo (accessed on March 2019).

  6. 6.

    http://spark.apache.org (accessed on March 2019).

  7. 7.

    The features have been learned on the ImageNet dataset, since pretrained networks are utilized. However, ImageNet is a comprehensive dataset, so the learned features can be used in other computer vision tasks (i.e., NDVR) without the need of retraining.

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Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Giorgos Kordopatis-Zilos, Symeon Papadopoulos & Ioannis Kompatsiaris

  2. School of Electronic Engineering and Computer Science, Queen Mary University, London, UK

    Giorgos Kordopatis-Zilos & Ioannis Patras

Authors
  1. Giorgos Kordopatis-Zilos
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  2. Symeon Papadopoulos
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  3. Ioannis Patras
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  4. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Giorgos Kordopatis-Zilos .

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Editors and Affiliations

  1. Centre for Research and Technology Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Vasileios Mezaris

  2. MODUL Technology GmbH, MODUL University Vienna, Vienna, Austria

    Lyndon Nixon

  3. Centre for Research and Technology Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Symeon Papadopoulos

  4. Agence France-Presse, Paris, France

    Denis Teyssou

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Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., Kompatsiaris, I. (2019). Finding Near-Duplicate Videos in Large-Scale Collections. In: Mezaris, V., Nixon, L., Papadopoulos, S., Teyssou, D. (eds) Video Verification in the Fake News Era. Springer, Cham. https://doi.org/10.1007/978-3-030-26752-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-26752-0_4

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