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Semantic Modeling and Knowledge Representation for Multimedia Data

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Encyclopedia of Database Systems

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Synonyms

Multimedia information retrieval; Image/Video/Music search

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.

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The principal design...

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Recommended Reading

  1. Aizerman M.A., Braverman E.M., and Rozonoer L.I. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25:821–837, 1964.

    MathSciNet  Google Scholar 

  2. Barnard K. and Forsyth D. Learning the semantics of words and pictures. Int. Conf. on Computer Vision, 2:408–415, 2000.

    Google Scholar 

  3. Beyer K., Goldstein J., Ramakrishnan R., and Shaft U. When is Nearest Neighbor meaningful. In Proc. 7th Int. Conf. on Database Theory, 1999, pp. 217–235.

    Google Scholar 

  4. Blei D.M., Ng A., and Jordan M. Latent Dirichlet allocation. J. Machine Learning Res. 3, 2003.

    Google Scholar 

  5. Chang E.Y. et al. Parallelizing support vector machines on distributed computers. In Proc. Advances in Neural Inf. Proc. Syst. 20, Proc. 21st Annual Conf. on Neural Inf. Proc. Syst., 2007.

    Google Scholar 

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

    Google Scholar 

  7. Donoho D.L. Aide-Memoire. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality (American Math. Society Lecture). In Math Challenges of the 21st Century Conference, 2000.

    Google Scholar 

  8. Flickner M. et al. Query by Image and Video Content: QBIC system. IEEE Comput., (28), 1995.

    Google Scholar 

  9. Goh K., Chang E.Y., and Lai W.-C. Concept-dependent multimodal active learning for image retrieval. In Proc. 12th ACM Int. Conf. on Multimedia, 2004, pp. 564–571.

    Google Scholar 

  10. Hoi C.-H. and Lyu M.R. A novel log-based relevance feedback technique in content-based image retrieval. In Proc. 12th ACM Int. Conf. on Multimedia, 2004, pp. 24–31.

    Google Scholar 

  11. Li B. and Chang E.Y. Discovery of a perceptual distance function for measuring image similarity. ACM Multimedia Syst. J. (Special Issue on Content-Based Image Retrieval), 8(6):512–522, 2003.

    Google Scholar 

  12. Rocchio J.J. Relevance feedback in information retrieval. In The SMART Retrieval System – Experiments in Automatic Document Processing, G. Salton (ed.). Prentice-Hall, 1971, pp. 313–323.Chapter 14,

    Google Scholar 

  13. Rui Y., Huang T.S., and Chang S.-F. Image retrieval: current techniques, promising directions and open issues. J. Visual Commn. Image Representation, 1999.

    Google Scholar 

  14. Tong S. and Chang E.Y. Support vector machine active learning for image retrieval. In Proc. 9th ACM Int. Conf. on Multimedia, 2001, pp. 107–118.

    Google Scholar 

  15. Tversky A. Features of similarity. Psychol. Rev., 84:327–352, 1997.

    Google Scholar 

  16. Wu G. and Chang E.Y. KBA: Kernel Boundary Alignment considering imbalanced data distribution. IEEE Trans. Knowl. Data Eng., 17(6):786–795, 2005.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Google Research, Mountain View, CA, USA

    Edward Y. Chang

Authors
  1. Edward Y. Chang
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Chang, E.Y. (2009). Semantic Modeling and Knowledge Representation for Multimedia Data. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1038

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