Cut-Region: A Compact Building Block for Hierarchical Metric Indexing

  • Jakub Lokoč
  • Přemysl Čech
  • Jiří Novák
  • Tomáš Skopal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7404)

Abstract

With the emerging applications dealing with complex multimedia retrieval, such as the multimedia exploration, appropriate indexing structures need to be designed. A formalism for compact metric region description can significantly simplify the design of algorithms for such indexes, thus more complex and efficient metric indexes can be developed. In this paper, we introduce the cut-regions that are suitable for compact metric region description and we discuss their basic operations. To demonstrate the power of cut-regions, we redefine the PM-Tree using the cut-region formalism and, moreover, we use the formalism to describe our new improvements of the PM-Tree construction techniques. We have experimentally evaluated that the improved construction techniques lead to query performance originally obtained just using expensive construction techniques. Also in comparison with other metric and spatial access methods, the revisited PM-Tree proved its benefits.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beecks, C., Lokoč, J., Seidl, T., Skopal, T.: Indexing the signature quadratic form distance for efficient content-based multimedia retrieval. In: Proc. ACM International Conference on Multimedia Retrieval (2011)Google Scholar
  2. 2.
    Beecks, C., Skopal, T., Schoeffmann, K., Seidl, T.: Towards large-scale multimedia exploration. In: Das, G., Hsristidis, V., Ilyas, I. (eds.) Proceedings of the 5th International Workshop on Ranking in Databases (DBRank 2011), pp. 31–33. VLDB, Seattle, WA, USA (2011)Google Scholar
  3. 3.
    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
  4. 4.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, E.C., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. Pattern Anal. Mach. Intell. 30(9), 1647–1658 (2008)CrossRefGoogle Scholar
  6. 6.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In: VLDB 1997, pp. 426–435 (1997)Google Scholar
  7. 7.
    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
  8. 8.
    Hetland, M.L.: The Basic Principles of Metric Indexing. In: Coello, C.A.C., Dehuri, S., Ghosh, S. (eds.) Swarm Intelligence for Multi-objective Problems in Data Mining. SCI, vol. 242, pp. 199–232. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Hettich, S., Bay, S.D.: The UCI KDD archive (1999), http://kdd.ics.uci.edu
  10. 10.
    Jakub, L., Tomáš, S.: On Reinsertions in M-tree. In: SISAP 2008: Proceedings of the First International Workshop on Similarity Search and Applications (SISAP 2008), pp. 121–128. IEEE Computer Society, Washington, DC (2008)Google Scholar
  11. 11.
    Mico, M.L., Oncina, J., Vidal, E.: A new version of the nearest-neighbour approximating and eliminating search algorithm (aesa) with linear preprocessing time and memory requirements. Pattern Recogn. Lett. 15(1), 9–17 (1994)CrossRefGoogle Scholar
  12. 12.
    Novak, D., Batko, M., Zezula, P.: Metric index: An efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011)CrossRefGoogle Scholar
  13. 13.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann (2006)Google Scholar
  14. 14.
    Skopal, T.: Pivoting M-tree: A Metric Access Method for Efficient Similarity Search. In: Proceedings of the 4th Annual Workshop DATESO, Desná, Czech Republic, pp. 21–31 (2004) ISBN 80-248-0457-3; also available at CEUR, vol. 98, ISSN 1613-0073, http://www.ceur-ws.org/Vol-98
  15. 15.
    Skopal, T.: Unified framework for fast exact and approximate search in dissimilarity spaces. ACM Transactions on Database Systems 32(4), 1–46 (2007)CrossRefGoogle Scholar
  16. 16.
    Skopal, T., Lokoč, J.: New Dynamic Construction Techniques for M-tree. Journal of Discrete Algorithms 7(1), 62–77 (2009)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Skopal, T., Lokoč, J.: Answering Metric Skyline Queries by PM-tree. In: Proceedings of the Dateso 2010 Workshop, vol. 567, pp. 22–37. Matfyz Press (2010)Google Scholar
  18. 18.
    Skopal, T., Pokorný, J., Snášel, V.: Nearest Neighbours Search Using the PM-Tree. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 803–815. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jakub Lokoč
    • 1
  • Přemysl Čech
    • 1
  • Jiří Novák
    • 1
  • Tomáš Skopal
    • 1
  1. 1.SIRET research group, Dept. of Software Engineering, Faculty of Mathematics and PhysicsCharles University in PragueCzech Republic

Personalised recommendations