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Big Data Analytics for Large-Scale Multimedia Search

Om Big Data Analytics for Large-Scale Multimedia Search

A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability 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 LargeΓÇôScale Multimedia Search covers: representation learning, concept and eventΓÇôbased video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, largeΓÇôscale 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 WileyΓÇôhosted BCS that features links to open source algorithms, data sets and tools Big Data Analytics for LargeΓÇôScale 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.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9781119376972
  • Bindende:
  • Hardback
  • Sider:
  • 376
  • Utgitt:
  • 19. april 2019
  • Dimensjoner:
  • 163x244x25 mm.
  • Vekt:
  • 839 g.
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 27. mars 2026

Beskrivelse av Big Data Analytics for Large-Scale Multimedia Search

A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability
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 LargeΓÇôScale Multimedia Search covers: representation learning, concept and eventΓÇôbased video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, largeΓÇôscale 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 WileyΓÇôhosted BCS that features links to open source algorithms, data sets and tools Big Data Analytics for LargeΓÇôScale 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.

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