Advertisement

Mediamill: Advanced Browsing in News Video Archives

  • Marcel Worring
  • Cees Snoek
  • Ork de Rooij
  • Giang Nguyen
  • Richard van Balen
  • Dennis Koelma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)

Abstract

In this paper we present our Mediamill video search engine. The basis for the engine is a semantic indexing process which derives a lexicon of 101 concepts. To support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread space, and browsers to explore them. It extends upon [1] with improved browsing tools. The search system is evaluated within the TRECVID benchmark [2]. We obtain a top-3 result for 19 out of 24 search topics. In addition, we obtain the highest mean average precision of all search participants.

Keywords

Video Retrieval Similarity Space Search Topic Textual Query Multimedia Information Retrieval 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Snoek, C., Worring, M., van Gemert, J., Geusebroek, J., Koelma, D., Nguyen, G., de Rooij, O., Seinstra, F.: Mediamill: Exploring news video archives based on learned semantics. In: ACM Multimedia, Singapore (2005)Google Scholar
  2. 2.
    Smeaton, A.: Large scale evaluations of multimedia information retrieval: The TRECVid experience. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 11–17. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Christel, M., Hauptmann, A.: The use and utility of high-level semantic features in video retrieval. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 134–144. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Rautiainen, M., Ojala, T., Seppnen, T.: Clustertemporal browsing of large news video databases. In: IEEE International Conference on Multimedia and Expo. (2004)Google Scholar
  5. 5.
    Adcock, J., Cooper, M., Girgensohn, A., Wilcox, L.: Interactive video search using multilevel indexing. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 205–214. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Heesch, D., Rüger, S.: Three interfaces for content-based access to image collections. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 491–499. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Geusebroek, J.: Distinctive and compact color featuress for object recognition (2005) (Submitted for publication)Google Scholar
  8. 8.
    Snoek, C., et al.: The MediaMill TRECVID 2005 semantic video search engine. In: Proc. TRECVID Workshop. NIST (2005)Google Scholar
  9. 9.
    Nguyen, G., Worring, M.: Similarity based visualization of image collections. In: Proceedings of 7th International Workshop on Audio-Visual Content and Information Visualization in Digital Libraries (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcel Worring
    • 1
  • Cees Snoek
    • 1
  • Ork de Rooij
    • 1
  • Giang Nguyen
    • 1
  • Richard van Balen
    • 1
  • Dennis Koelma
    • 1
  1. 1.Intelligent Systems Lab AmsterdamUniversity of AmsterdamAmsterdamThe Netherlands

Personalised recommendations