SpringerLink
Log in

Multimedia Data Indexing

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 99 Accesses

Synonyms

MM indexing

Definition

Multimedia (MM) data indexing refers to the problem of preprocessing a database of MM objects so that they can be efficiently searched for on the basis of their content. Due to the nature of MM data, indexing solutions are needed to efficiently support similarity queries, where the similarity of two objects is usually defined by some expert of the domain and can vary depending on the specific application. Peculiar features of MM indexing are the intrinsic high-dimensional nature of the data to be organized and the complexity of similarity criteria that are used to compare objects. Both aspects are therefore to be considered for designing efficient indexing solutions.

Historical Background

Earlier approaches to the problem of MM data indexing date back to the beginning of 1990s, when it became apparent the need of efficiently supporting queries on large collections of nonstandard data types, such as images and time series. Representing the content of such...

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

Access this chapter

Log in via an institution

Institutional subscriptions

Recommended Reading

  1. Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases. In: Proceedings of the 4th International Conference on Foundations of Data Organizations and Algorithms; 1993. p. 69–84.

    Google Scholar 

  2. Bartolini I, Ciaccia P, Patella M. WARP: accurate retrieval of shapes using phase of Fourier descriptors and time war** distance. IEEE Trans Pattern Anal Machine Intell. 2005;27(1):142–7.

    Article  Google Scholar 

  3. Chávez E, Navarro G, Baeza-Yates R, Marroquín JS. Proximity searching in metric spaces. ACM Comput Surv. 2001;33(3):273–321.

    Article  Google Scholar 

  4. Ciaccia P, Patella M. Searching in metric spaces with user-defined and approximate distances. ACM Trans Database Syst. 2002;27, 398(4):–437.

    Google Scholar 

  5. Ciaccia P, Patella M, Zezula P. M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of the 23th International Conference on Very Large Data Bases; 2007. p. 426–35.

    Google Scholar 

  6. Faloutsos C, Barber R, Flickner M, Hafner J, Niblack W, Petkovic D, Equitz W. Efficient and effective querying by image content. J Intell Inf Sys. 1994;3(3/4):231–62.

    Article  Google Scholar 

  7. Goyal N, Lifshits Y, Schütse H. Disorder inequality: a combinatorial approach to nearest neighbor search. In: Proceedings of the 1st ACM International Conference on Web Search and Data Mining; 2008. p. 25–32.

    Google Scholar 

  8. Jagadish HV. A retrieval technique for similar shapes. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1991. p. 208–17.

    Google Scholar 

  9. Keogh E. Exact indexing of dynamic time war**. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 406–17.

    Google Scholar 

  10. Lee J, Oh JH, Hwang S. STRG-index: spatio-temporal region graph indexing for large video databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2005. p. 718–29.

    Google Scholar 

  11. Skopal T. On fast non-metric similarity search by metric access methods. In: Advances in Database Technology, Proceedings of the 10th International Conference on Extending Database Technology; 2006. p. 718–36.

    Google Scholar 

  12. Skopal T, Hoksza D. Improving the performance of M-tree family by nearest-neighbor graphs. In: Proceedings of the 11th East European Conference Advances in Databases and Information Systems; 2007. p. 172–88.

    Google Scholar 

  13. Vlachos M, Vagena Z, Yu PS, Athitsos V. Rotation invariant indexing of shapes and line drawings. In: Proceedings of the ACM International Conference on Information and Knowledge Management; 2005. p. 131–8.

    Google Scholar 

  14. Zezula P, Amato G, Dohnal V, Batko M. Similarity search: the metric space approach. Berlin: Springer; 2005.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, University of Bologna, Bologna, Italy

    Paolo Ciaccia

Authors
  1. Paolo Ciaccia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paolo Ciaccia .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science, New Jersey Inst. of Technology, University Heights, 7102, Newark, NJ, USA

    Vincent Oria

  2. Digital Content and Media Sciences ReseaMultimedia Information Research Division, National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this entry

Cite this entry

Ciaccia, P. (2017). Multimedia Data Indexing. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_1037-2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_1037-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation