Advertisement

Association Rule Mining of Multimedia Content

  • Adalbert F. X. WilhelmEmail author
  • Arne Jacobs
  • Thorsten Hermes
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The analysis of video sequences is of primary concern in the field of mass communication. One particular topic is the study of collective visual memories and neglections as they emerged in various cultures, with trans-cultural and global elements (Ludes P., Multimedia und Multi-Moderne: Schlüsselbilder, Fernsehnachrichten und World Wide Web – Medienzivilisierung in der Europäischen Währungsunion. Westdeutscher Verlag, Opladen 2001). The vast amount of visual data from television and web offerings make comparative studies on visual material rather complex and very expensive. A standard task in this realm is to find images that are similar to each other. Similarity is typically aimed at a conceptual level comprising both syntactic as well as semantic similarity. The use of semi-automatic picture retrieval techniques would facilitate this task. An important aspect is to combine the syntactical analysis that is usually performed automatically with the semantic level obtained from annotations or the analysis of captions or closely related text. Association rules are in particular suited to extract implicit knowledge from the data base and to make this knowledge accessible for further quantitative analysis.

Keywords

Video Sequence Association Rule Shot Boundary Video Scene Syntactic Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining associations between sets of items in massive databases. In Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data (pp. 207–216).Google Scholar
  2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules (IBM Research Report RJ9839).Google Scholar
  3. Ballard, D., & Brown, C. (1982). Computer vision. Englewoods Cliff: Prentice-Hall.Google Scholar
  4. Bruzzese, D., & Davino, C. (2001). Statistical pruning of discovered association rules. Computational Statistics, 16, 387–398.CrossRefMathSciNetGoogle Scholar
  5. Ding, Q., Ding, Q., & Perrizo, W. (2002). Association rule mining on remotely sensed images using p-trees. In: M.-S. Cheng, P. S. Yu, & B. Liu (Eds.), PAKDD Volume 2336 of Lecture notes in computer science (66–79). Berlin: Springer.Google Scholar
  6. Hermes, T., Miene, A., & Kreyenhop, P. (2000). On textures: A sketch of a texture-based image segmentation approach. In: R. Decker & W. Gaul (Eds.), Classification and information processing at the turn of the millenium (pp. 219–226). Berlin: Springer.Google Scholar
  7. Hermes, T., Miene, A., & Herzog, O. (2005). Graphical search for images by PictureFinder. International Journal of Multimedia Tools and Applications. Special Issue on Multimedia Retrieval Algorithmics, 27, 229–250.Google Scholar
  8. Jacobs, A., Miene, A, Ioannidis, G., & Herzog, O. (2004). Automatic shot boundary detection combining color, edge, and motion features of adjacent frames. In TREC Video Retrieval Evaluation Online Proceedings.Google Scholar
  9. Keller, R., Schlögel, A., Unwin, A., & Wilhelm, A. (2002). PISSARRO. Retrieved from http://stats.math.uni-augsburg.de/Pissarro.
  10. Lienhart, R. (1998). Comparison of automatic shot boundary detection algorithms. In: M. M. Yeoung, B.-L. Yeo, & C. A. Bouman (Eds.), Proc. SPIE, Storage and Retrieval for Image and Video Databases VII (Vol. 3656, pp. 290–301).Google Scholar
  11. Ordonez, C., & Omiecinski, E. (1999). Discovering association rules based on image content. In ADL (pp. 38–49).Google Scholar
  12. Perner, P. (Ed.) (2004). Advances in data mining, applications in image mining, medicine and biotechnology, management and environmental control, and telecommunications, 4th industrial conference on Data Mining, ICDM 2004, Leipzig, Germany, July 4–7, 2004, Revised Selected Papers. Berlin: Springer.Google Scholar
  13. Tešić, J. (2004). Managing large-scale multimedia repositories. Ph.D. Thesis, University of California, Santa Barbara.Google Scholar
  14. Yam, C.-Y., Nixon, M. S., & Carter, J. N. (2004). Automated person recognition by walking and running via model-based approaches. Pattern Recognition, 37, 1057–1072.CrossRefGoogle Scholar
  15. Yusoff, Y., Christmas, W.-J., & Kittler, J. (1998). A study on automatic shot change detection. In: D. Hutchison & R. Schäfer, (Eds.), ECMAST, volume 1425 of Lecture Notes in Computer Science (pp. 177–189). Berlin: Springer.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adalbert F. X. Wilhelm
    • 1
    Email author
  • Arne Jacobs
  • Thorsten Hermes
  1. 1.Jacobs University BremenBremenGermany

Personalised recommendations