Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Multimedia Information Retrieval Model

  • Carlo Meghini
  • Fabrizio Sebastiani
  • Umberto Straccia
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_233

Synonyms

Content-based retrieval; Multimedia information discovery; Semantic-based retrieval

Definition

Given a collection of multimedia documents, the goal of multimedia information retrieval (MIR) is to find the documents that are relevant to a user information need. A multimedia document is a complex information object, with components of different kinds, such as text, images, video and sound, all in digital form.

Historical Background

The vast body of knowledge nowadays labeled as MIR, is the product of several streams of research, which have arisen independently of each others and proceeded largely in an autonomous way, until the beginning of 2000, when the difficulty of the problem and the lack of effective results made it evident that success could be achieved only through integration of methods. These streams can be grouped into three main areas:

The first area is that of information retrieval(IR) proper. The notion of IR attracted significant scientific interest from the...

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Recommended Reading

  1. 1.
    Baader F, Calvanese D, McGuiness D, Nardi D, Patel-Scheneider P, editors. The description logic handbook. Cambridge: Cambridge University Press; 2003.zbMATHGoogle Scholar
  2. 2.
    Bach JR, Fuller C, Gupta A, Hampapur A, Horowitz B, Humphrey R, Jain R, Shu C.-F. The Virage image search engine: an open framework for image management. In: Proceedings of the 4th SPIE Conference on Storage and Retrieval for Still Images and Video Databases; 1996. p. 76–87.Google Scholar
  3. 3.
    Candela L, Castelli D, Pagano P, Thanos C Ioannidis Y, Koutrika G., Ross S, Schek HJ, Schuldt H. Setting the foundations of digital libraries. The DELOS manifesto. D-Lib Magazine. 13(3/4), March/April 2007.Google Scholar
  4. 4.
    Crestani F, Lalmas M, van Rijsbergen CJ,.editors. Logic and uncertainty in information retrieval: advanced models for the representation and retrieval of information. The Kluwer International Series On Information Retrieval 4. Boston: Kluwer Academic; 1998.Google Scholar
  5. 5.
    Davidson D. Truth and meaning. In: Inquiries into truth and interpretation. Oxford: Clarendon; 1991. p. 17–36.Google Scholar
  6. 6.
    Del Bimbo A. Visual information retrieval. Los Altos: Morgan Kaufmann; 1999.Google Scholar
  7. 7.
    Faloutsos C, Barber R, Flickner M, Hafner J, Niblack W. Efficient and effective querying by image content. J Intell Inform Syst. 1994;3(3–4):231–62.CrossRefGoogle Scholar
  8. 8.
    Liu F, Picard RW. Periodicity, directionality, and randomness: wold features for image modelling and retrieval. IEEE Trans Pattern Analysis Machine Intell. 1996;18(7):722–33.CrossRefGoogle Scholar
  9. 9.
    Manning CD, Raghavan P, Schütze H. An introduction to information retrieval. Cambridge: Cambridge University Press; 2007.zbMATHGoogle Scholar
  10. 10.
    Meghini C, Sebastiani F, Straccia U. A model of multimedia information retrieval. J ACM. 2001;48(5):909–70.MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Petrakis EG, Faloutsos C. Similarity searching in medical image databases. IEEE Trans Data Knowl Eng. 1997;9(3):435–47.CrossRefGoogle Scholar
  12. 12.
    Ravela S, Manmatha R. Image retrieval by appearance. In: Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1997. p. 278–85.Google Scholar
  13. 13.
    Rui Y, Huang TS, Ortega M, Mehrotra S. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Tech. 1998;8(5):644–55.CrossRefGoogle Scholar
  14. 14.
    Smith JR, Chang S-F. Transform features for texture classification and discrimination in large image databases. In: Proceedings of the International Conference Image Processing; 1994. p. 407–11.Google Scholar
  15. 15.
    Zezula P, Amato G, Dohnal V, Batko M. Similarity search: the metric approach. Berlin: Springer; 2006.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Carlo Meghini
    • 1
  • Fabrizio Sebastiani
    • 2
  • Umberto Straccia
    • 1
  1. 1.The Italian National Research CouncilPisaItaly
  2. 2.Qatar Computing Research InstituteDohaQatar

Section editors and affiliations

  • Jeffrey Xu Yu
    • 1
  1. 1.The Chinese University of Hong KongHong KongChina