Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Database Systems pp 3399–3405Cite as

Semantic Modeling and Knowledge Representation for Multimedia Data

  • Reference work entry
  • First Online:

  • 159 Accesses

Synonyms

Image/Video/Music search; Multimedia information retrieval

Definition

Semantic modeling and knowledge representation is essential to a multimedia information retrieval system for supporting effective data organization and search. Semantic modeling and knowledge representation for multimedia data (e.g., imagery, video, and music) consists of three steps: feature extraction, semantic labeling, and features-to-semantics mapping. Feature extraction obtains perceptual characteristics such as color, shape, texture, salient-object, and motion features from multimedia data; semantic labeling associates multimedia data with cognitive concepts; and features-to-semantics mapping constructs correspondence between perceptual features and cognitive concepts. Analogically to data representation for text documents, improving semantic modeling and knowledge representation for multimedia data leads to enhanced data organization and query performance.

Historical Background

The principal design...

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

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

Learn about institutional subscriptions

Recommended Reading

  1. Aizerman MA, Braverman EM, Rozonoer LI. Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control. 1964;25:821–37.

    MATH  Google Scholar 

  2. Barnard K, Forsyth D. Learning the semantics of words and pictures. Int Conf Comput Vision. 2000;2:408–15.

    Article  Google Scholar 

  3. Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is nearest neighbor meaningful. In: Proceedings of the 7th International Conference on Database Theory; 1999. p. 217–35.

    Google Scholar 

  4. Blei DM, Ng A, Jordan M. Latent Dirichlet allocation. J Machine Learning Res. 2003;3(4/5):993–1022.

    Google Scholar 

  5. Chang EY, et al. Parallelizing support vector machines on distributed computers. In: Advances in Neural Information Proceedings of the Systems 20, Proceedings of the 21st Annual Conference on Neural Information Proceedings of the Systems; 2007.

    Google Scholar 

  6. Datta R, Joshi D, Li J, Wang JZ. Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv. 2008;40(65).

    Article  Google Scholar 

  7. Donoho DL. Aide-Memoire. High-dimensional data analysis: the curses and blessings of dimensionality (American Math. Society Lecture). In: Mathematical Challenges Of The 21st Century Explored at American Mathematical Society Conference; 2000.

    Google Scholar 

  8. Flickner M, et al. Query by image and video content: QBIC system. IEEE Comput. 1995;28(9).

    Google Scholar 

  9. Goh K, Chang EY, Lai W-C. Concept-dependent multimodal active learning for image retrieval. In: Proceedings of the 12th ACM International Conference on Multimedia; 2004. p. 564–71.

    Google Scholar 

  10. Hoi C-H, Lyu MR. A novel log-based relevance feedback technique in content-based image retrieval. In: Proceedings of the 12th ACM International Conference on Multimedia; 2004. p. 24–31.

    Google Scholar 

  11. Li B, Chang EY. Discovery of a perceptual distance function for measuring image similarity. ACM Multimedia Syst J. (Special Issue on Content-Based Image Retrieval). 2003;8(6):512–22.

    Article  Google Scholar 

  12. Rocchio JJ. Relevance feedback in information retrieval. In: Salton G, editor. The SMART retrieval system – experiments in automatic document processing. Englewood Cliffs: Prentice-Hall; 1971. p. 313–23. Chapter 14.

    Google Scholar 

  13. Rui Y, Huang TS, Chang S-F. Image retrieval: current techniques, promising directions and open issues. J Visual Commn Image Represent. 1999;10(1):39–62.

    Article  Google Scholar 

  14. Tong S, Chang EY. Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM International Conference on Multimedia; 2001. p. 107–18.

    Google Scholar 

  15. Tversky A. Features of similarity. Psychol Rev. 1997;84(4):327–52.

    Article  Google Scholar 

  16. Wu G, Chang EY. KBA: Kernel Boundary Alignment considering imbalanced data distribution. IEEE Trans Knowl Data Eng. 2005;17(6):786–95.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Google Research, Mountain View, CA, USA

    Edward Y. Chang

Authors
  1. Edward Y. Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edward Y. Chang .

Editor information

Editors and Affiliations

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

    Ling Liu

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

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science, New Jersey Inst. of Technology, 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

© 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

Chang, E.Y. (2018). Semantic Modeling and Knowledge Representation for Multimedia Data. 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_1038

Download citation

Publish with us

Policies and ethics

Search

Navigation