Video similarity measurement and search

Saddam Bekhet; M. Hassaballah; Amr Ahmed; Ali H. Ahmed

doi:10.1007/978-3-030-03000-1_4

Video similarity measurement and search

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

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Verlag
Pages	85-112
Number of pages	28
DOIs	https://doi.org/10.1007/978-3-030-03000-1_4
Publication status	Published - 1 Jan 2019

Publication series

Name	Studies in Computational Intelligence
Volume	804
ISSN (Print)	1860-949X

Research Centres

Centre for Intelligent Visual Computing Research

Access to Document

10.1007/978-3-030-03000-1_4

Cite this

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Video similarity measurement and search

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