Fixed Height Queries Tree Permutation Index for Proximity Searching

  • Karina FigueroaEmail author
  • Rodrigo Paredes
  • J. Antonio Camarena-Ibarrola
  • Nora Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)


Similarity searching consists in retrieving from a database the objects, also known as nearest neighbors, that are most similar to a given query, it is a crucial task to several applications of the pattern recognition problem. In this paper we propose a new technique to reduce the number of comparisons needed to locate the nearest neighbors of a query. This new index takes advantage of two known algorithms: FHQT (Fixed Height Queries Tree) and PBA (Permutation-Based Algorithm), one for low dimension and the second for high dimension. Our results show that this combination brings out the best of both algorithms, this winner combination of FHQT and PBA locates nearest neighbors up to four times faster in high dimensions leaving the known well performance of FHQT in low dimensions unaffected.


  1. 1.
    Baeza-Yates, R.: Searching: an algorithmic tour. In: Kent, A., Williams, J. (eds.) Encyclopedia of Computer Science and Technology, vol. 37, pp. 331–359. Marcel Dekker Inc., New York (1997)Google Scholar
  2. 2.
    Baeza-Yates, R., Cunto, W., Manber, U., Wu, S.: Proximity matching using fixed-queries trees. In: Crochemore, M., Gusfield, D. (eds.) CPM 1994. LNCS, vol. 807, pp. 198–212. Springer, Heidelberg (1994). doi: 10.1007/3-540-58094-8_18 CrossRefGoogle 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).
  4. 4.
    Chávez, E., Figueroa, K.: Faster proximity searching in metric data. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 222–231. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24694-7_23 CrossRefGoogle Scholar
  5. 5.
    Chávez, E., Figueroa, K., Navarro, G.: Proximity searching in high dimensional spaces with a proximity preserving order. In: Gelbukh, A., Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 405–414. Springer, Heidelberg (2005). doi: 10.1007/11579427_41 CrossRefGoogle Scholar
  6. 6.
    Chávez, E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30(9), 1647–1658 (2009)Google Scholar
  7. 7.
    Chávez, E., Marroquín, J., Navarro, G.: Fixed queries array: a fast and economical data structure for proximity searching. Multimed. Tools Appl. (MTAP) 14(2), 113–135 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.: Proximity searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001)CrossRefGoogle Scholar
  9. 9.
    Figueroa, K., Paredes, R.: An effective permutant selection heuristic for proximity searching in metric spaces. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds.) MCPR 2014. LNCS, vol. 8495, pp. 102–111. Springer, Cham (2014). doi: 10.1007/978-3-319-07491-7_11 Google Scholar
  10. 10.
    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)zbMATHGoogle Scholar
  11. 11.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Karina Figueroa
    • 1
    Email author
  • Rodrigo Paredes
    • 2
  • J. Antonio Camarena-Ibarrola
    • 1
  • Nora Reyes
    • 3
  1. 1.Universidad MichoacanaMoreliaMexico
  2. 2.Universidad de TalcaTalcaChile
  3. 3.Universidad Nacional de San LuisSan LuisArgentina

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