Real-Time Exploration of Multimedia Collections

  • Juraj Moško
  • Tomáš Skopal
  • Tomáš Bartoš
  • Jakub Lokoč
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8506)

Abstract

With the huge expansion of smart devices and mobile applications, the ordinary users are consistently changing the conventional similarity search model. The users want to explore the multimedia data, so the typical query-by-example principle and the well-known keyword searching have become just a part of more complex retrieval processes. The emerging multimedia exploration systems with robust back-end retrieval system based on state of the art similarity search techniques provide a good solution. They enable interactive exploration process and implement exploration queries tightly connected with the user interface. However, they do not consider larger response times that might occur. To overcome this, we propose a scalable exploration system RTExp that allows evaluating the similarity queries in the near real time depending on user preferences (speed / precision). We describe building parts of the system and discuss various real-time characteristics for the exploration process. Also we provide results from the experimental evaluation of time-limited similarity queries and corresponding exploration operations.

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References

  1. 1.
    Bartoš, T., Skopal, T., Moško, J.: Towards Efficient Indexing of Arbitrary Similarity. SIGMOD Record 42(2), 5–10 (2013)CrossRefGoogle Scholar
  2. 2.
    Beecks, C., Skopal, T., Schöffmann, K., Seidl, T.: Towards large-scale multimedia exploration. In: DBRank 2011, Seattle, WA, USA, pp. 31–33 (2011)Google Scholar
  3. 3.
    Beecks, C., Uysal, M.S., Seidl, T.: Signature quadratic form distance. In: Conference on Image and Video Retrieval, CIVR 2010, pp. 438–445. ACM, New York (2010)Google Scholar
  4. 4.
    Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases. ACM 33(3), 322–373 (2001)Google Scholar
  5. 5.
    Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., Rabitti, F.: CoPhIR: A test collection for content-based image retrieval. CoRR abs/0905.4627v2 (2009)Google Scholar
  6. 6.
    Chávez, E., Navarro, G.: A probabilistic spell for the curse of dimensionality. In: Buchsbaum, A.L., Snoeyink, J. (eds.) ALENEX 2001. LNCS, vol. 2153, pp. 147–160. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In: VLDB 1997, pp. 426–435. Morgan Kaufmann Publishers Inc. (1997)Google Scholar
  8. 8.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Exper. 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  9. 9.
    Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The amsterdam library of object images. International Journal of Computer Vision 61(1), 103–112 (2005)CrossRefGoogle Scholar
  10. 10.
    Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. ACM Trans. Database Syst. 24(2), 265–318 (1999)CrossRefGoogle Scholar
  11. 11.
    Hjaltason, G., Samet, H.: Incremental Similarity Search in Multimedia Databases. Computer science technical report series, University of Maryland (2000)Google Scholar
  12. 12.
    Lokoč, J., Grošup, T., Skopal, T.: Image exploration using online feature extraction and reranking. In: ICMR 2012, pp. 66:1–66:2. ACM, New York (2012)Google Scholar
  13. 13.
    McIlvride, B.: Nine core requirements for real-time cloud systems (January 2012), http://real-timecloud.com/2012/01/12/nine-core-requirements-for-real-time-cloud-systems
  14. 14.
    Nguyen, G.P., Worring, M.: Interactive access to large image collections using similarity-based visualization. Journal of Visual Languages and Computing 19(2), 203–224 (2008)CrossRefGoogle Scholar
  15. 15.
    Patella, M., Ciaccia, P.: Approximate similarity search: A multi-faceted problem. J. of Discrete Algorithms 7(1), 36–48 (2009)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Santini, S., Jain, R.: Integrated browsing and querying for image databases. IEEE Multimedia 7(3), 26–39 (2000)CrossRefGoogle Scholar
  17. 17.
    Schaefer, G.: A next generation browsing environment for large image repositories. Multimedia Tools and Applications 47, 105–120 (2010), doi:10.1007/s11042-009-0409-2CrossRefGoogle Scholar
  18. 18.
    Schoeffmann, K., Ahlstrom, D., Beecks, C.: 3D Image Browsing on Mobile Devices. In: ISM 2011, pp. 335–336. IEEE Computer Society, Washington, DC (2011)Google Scholar
  19. 19.
    Skopal, T.: Unified framework for fast exact and approximate search in dissimilarity spaces. ACM Trans. Database Syst. 32(4) (November 2007)Google Scholar
  20. 20.
    Uhlmann, J.K.: Implementing Metric Trees to Satisfy General Proximity/Similarity Queries (1991) manuscriptGoogle Scholar
  21. 21.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer (2005)Google Scholar
  22. 22.
    Zezula, P., Savino, P., Amato, G., Rabitti, F.: Approximate similarity retrieval with m-trees. The VLDB Journal 7(4), 275–293 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Juraj Moško
    • 1
  • Tomáš Skopal
    • 1
  • Tomáš Bartoš
    • 1
  • Jakub Lokoč
    • 1
  1. 1.Faculty of Mathematics and Physics, SIRET Research GroupCharles University in PraguePragueCzech Republic

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