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Video Similarity Measurement and Search

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Recent Advances in Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 804))

Abstract

The quantity of digital videos is huge, due to technological advances in video capture, storage and compression. However, the usefulness of these enormous volumes is limited by the effectiveness of content-based video retrieval systems (CBVR). Video matching for the retrieval purpose is the core of these CBVR systems where videos are matched based on their respective visual features and their evolvement across video frames. Also, it acts as an essential foundational layer to infer semantic similarity at advanced stage, in collaboration with metadata. This chapter presents and discusses the core field concepts, problems and recent trends. This will provide the reader with the required amount of knowledge to select suitable features’ set and adequate techniques to develop robust research in this field.

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Authors and Affiliations

  1. Faculty of Commerce, South Valley University, Qena, Egypt

    Saddam Bekhet

  2. Faculty of Computers and Information, Computer Science Department, South Valley University, Luxor, Egypt

    M. Hassaballah

  3. University of Nottingham (Malaysia Campus), Nottingham, Malaysia

    Amr Ahmed

  4. University of Assuit, Assuit, Egypt

    Ali H. Ahmed

Authors
  1. Saddam Bekhet
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  2. M. Hassaballah
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  3. Amr Ahmed
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  4. Ali H. Ahmed
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Corresponding author

Correspondence to Saddam Bekhet .

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Editors and Affiliations

  1. Department of Computer Science, South Valley University, Luxor, Egypt

    Mahmoud Hassaballah

  2. Department of Information Technology, Zagazig University, Zagazig, Egypt

    Khalid M. Hosny

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Bekhet, S., Hassaballah, M., Ahmed, A., Ahmed, A.H. (2019). Video Similarity Measurement and Search. In: Hassaballah, M., Hosny, K. (eds) Recent Advances in Computer Vision. Studies in Computational Intelligence, vol 804. Springer, Cham. https://doi.org/10.1007/978-3-030-03000-1_4

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