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Finding Semantically Related Videos in Closed Collections

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Video Verification in the Fake News Era

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Abstract

Modern newsroom tools offer advanced functionality for automatic and semi-automatic content collection from the web and social media sources to accompany news stories. However, the content collected in this way often tends to be unstructured and may include irrelevant items. An important step in the verification process is to organize this content, both with respect to what it shows, and with respect to its origin. This chapter presents our efforts in this direction, which resulted in two components. One aims to detect semantic concepts in video shots, to help annotation and organization of content collections. We implement a system based on deep learning, featuring a number of advances and adaptations of existing algorithms to increase performance for the task. The other component aims to detect logos in videos in order to identify their provenance. We present our progress from a keypoint-based detection system to a system based on deep learning.

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Notes

  1. 1.

    https://www.nationsencyclopedia.com/WorldStats/CIA-Television-broadcast-stations.html, accessed 08 April 2019.

  2. 2.

    https://github.com/markatopoulou/fvmtl-ccelc.

  3. 3.

    https://github.com/rbgirshick/py-faster-rcnn/.

  4. 4.

    https://github.com/tensorflow/models/tree/master/research/object_detection/.

  5. 5.

    http://press.liacs.nl/mirflickr/.

  6. 6.

    http://opencv.org/.

  7. 7.

    http://www.cs.ubc.ca/research/flann/.

  8. 8.

    https://github.com/rbgirshick/py-faster-rcnn.

  9. 9.

    See footnote 4.

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Authors and Affiliations

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

    Foteini Markatopoulou, Markos Zampoglou, Evlampios Apostolidis, Symeon Papadopoulos, Vasileios Mezaris & Ioannis Kompatsiaris

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

    Evlampios Apostolidis & Ioannis Patras

Authors
  1. Foteini Markatopoulou
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  2. Markos Zampoglou
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Correspondence to Markos Zampoglou .

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  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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Markatopoulou, F. et al. (2019). Finding Semantically Related Videos in Closed 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_5

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