Skip to main content

Multimedia Information Retrieval Model

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

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...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Baader F, Calvanese D, McGuiness D, Nardi D, Patel-Scheneider P, editors. The description logic handbook. Cambridge: Cambridge University Press; 2003.

    MATH  Google Scholar 

  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. 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. 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. Davidson D. Truth and meaning. In: Inquiries into truth and interpretation. Oxford: Clarendon; 1991. p. 17–36.

    Google Scholar 

  6. Del Bimbo A. Visual information retrieval. Los Altos: Morgan Kaufmann; 1999.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  9. Manning CD, Raghavan P, Schütze H. An introduction to information retrieval. Cambridge: Cambridge University Press; 2007.

    MATH  Google Scholar 

  10. Meghini C, Sebastiani F, Straccia U. A model of multimedia information retrieval. J ACM. 2001;48(5):909–70.

    Article  MathSciNet  MATH  Google Scholar 

  11. Petrakis EG, Faloutsos C. Similarity searching in medical image databases. IEEE Trans Data Knowl Eng. 1997;9(3):435–47.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. Zezula P, Amato G, Dohnal V, Batko M. Similarity search: the metric approach. Berlin: Springer; 2006.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlo Meghini .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

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

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Meghini, C., Sebastiani, F., Straccia, U. (2018). Multimedia Information Retrieval Model. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_233

Download citation

Publish with us

Policies and ethics