Video similarity measurement and search

Saddam Bekhet*, M. Hassaballah, Amr Ahmed, Ali H. Ahmed

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages28
Publication statusPublished - 1 Jan 2019

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

Research Centres

  • Centre for Intelligent Visual Computing Research


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