Efficient Group of Permutants for Proximity Searching

  • Karina Figueroa Mora
  • Rodrigo Paredes
  • Roberto Rangel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

Modeling proximity searching problems in a metric space allows one to approach many problems in different areas, e.g. pattern recognition, multimedia search, or clustering. Recently there was proposed the permutation based approach, a novel technique that is unbeatable in practice but difficult to compress. In this article we introduce an improvement on that metric space search data structure. Our technique shows that we can compress the permutation based algorithm without loosing precision. We show experimentally that our technique is competitive with the original idea and improves it up to 46% in real databases.

Keywords

Original Idea Modeling Proximity Real Database Synthetic Database Machine Word 
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. 1.
    Chávez, E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) 30(9), 1647–1658 (2009)Google Scholar
  2. 2.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  3. 3.
    Esuli, A.: Mipai: using the pp-index to build an efficient and scalable similarity search system. In: Similary Searching and Applications, pp. 146–148 (2009)Google Scholar
  4. 4.
    Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Falchi, F., Kacimi, M., Mass, Y., Rabitti, F., Zezula, P.: Sapir: Scalable and distributed image searching. In: CEUR Workshop Proceedings SAMT (Posters and Demos), vol. 300, pp. 11–12 (2007)Google Scholar
  6. 6.
    Hjaltason, G., Samet, H.: Index-driven similarity search in metric spaces. ACM Transactions Database Systems 28(4), 517–580 (2003)CrossRefGoogle Scholar
  7. 7.
    Sadit, E., Chávez, E.: On locality sensitive hashing in metric spaces. In: Similarity Search and Applications, pp. 67–74. ACM Press, New York (2010), ISBN: 978-1-4503-0420-7Google Scholar
  8. 8.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling). Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  9. 9.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. In: Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Karina Figueroa Mora
    • 1
  • Rodrigo Paredes
    • 2
  • Roberto Rangel
    • 1
  1. 1.Universidad Michoacana de San Nicolás de HidalgoMéxico
  2. 2.Universidad de TalcaChile

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