Multimedia Retrieval

  • Henk M. Blanken
  • Henk Ernst Blok
  • Ling Feng
  • Arjen P. de Vries

Part of the Data-Centric Systems and Applications book series (DCSA)

Table of contents

  1. Front Matter
    Pages I-XVIII
  2. Henk Blanken, Ling Feng, Maurice van Keulen, Henk Ernst Blok
    Pages 1-22
  3. Ling Feng, Rogier Brussee, Henk Blanken, Mettina Veenstra
    Pages 23-51
  4. Elena Ranguelova, Mark Huiskes
    Pages 53-95
  5. Henk Blanken, Djoerd Hiemstra
    Pages 97-124
  6. Ferdi van der Heijden, Luuk Spreeuwers
    Pages 125-175
  7. Thijs Westerveld, Arjen de Vries, Franciska de Jong
    Pages 177-198
  8. Roeland Ordelman, Franciska de Jong, David van Leeuwen
    Pages 199-224
  9. Cees G. M. Snoek, Marcel Worring, Jan-Mark Geusebroek, Dennis C. Koelma, Frank J. Seinstra, Arnold W. M. Smeulders
    Pages 225-249
  10. Milan Petković, Willem Jonker, Henk Blanken
    Pages 251-269
  11. Vojkan Mihajlović, Milan Petković, Willem Jonker, Henk Blanken
    Pages 271-294
  12. Erik Boertjes, Anton Nijholt
    Pages 295-320
  13. Paul Koster, Willem Jonker
    Pages 321-345
  14. Djoerd Hiemstra, Wessel Kraaij
    Pages 347-366
  15. Back Matter
    Pages 367-372

About this book


Retrieval of multimedia data is different from retrieval of structured data. A key problem in multimedia databases is search, and the proposed solutions to the problem of multimedia information retrieval span a rather wide spectrum of topics outside the traditional database area, ranging from information retrieval and human–computer interaction to computer vision and pattern recognition.

Based on more than 10 years of teaching experience, Blanken and his coeditors have assembled all the topics that should be covered in advanced undergraduate or graduate courses on multimedia retrieval and multimedia databases. The single chapters of this textbook explain the general architecture of multimedia information retrieval systems; various metadata languages like Dublin Core, RDF, or MPEG; pattern recognition through Markov models, unsupervised learning, and pattern clustering; various indexing approaches to audio and video streams; interaction and control; the protection of content and user privacy; and search effectiveness and efficiency. The authors emphasize high-level features and show how these features are used in mathematical models to support the retrieval process. For each chapter, there’s detail on further reading, and additional exercises and teaching material is available online.


Audio Data Computer Vision Digital Rights Management Image Data Image Processing MPEG Markov Model Multimedia Multimedia Retrieval Pattern Recognition Speech Indexing Video Indexing Video Retrieval cognition learning

Editors and affiliations

  • Henk M. Blanken
    • 1
  • Henk Ernst Blok
    • 1
  • Ling Feng
    • 1
  • Arjen P. de Vries
    • 2
  1. 1.Database group Faculty of EWIUniversity of TwenteEnschedeThe Netherlands
  2. 2.Centrum voor Wiskunde en InformaticaAmsterdamThe Netherlands

Bibliographic information

Industry Sectors
Finance, Business & Banking
IT & Software
Consumer Packaged Goods