Big data analytics for large-scale multimedia search

Vrochidis, Stefanos; Huet, Benoit; Chang, Edward Y.; Kompatsiaris, Ioannis
Wiley, June 2019, ISBN: 978-1119376972

The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections.

Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data.

  • Addresses the area of multimedia retrieval and pays close attention to the issue of scalability
  • Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios
  • Includes tables, illustrations, and figures
  • Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools

Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.

Data Science
Eurecom Ref:
© Wiley. Personal use of this material is permitted. The definitive version of this paper was published in Wiley, June 2019, ISBN: 978-1119376972 and is available at :