Multimedia Tools and Applications

, Volume 61, Issue 1, pp 51–68 | Cite as

Tertiary hash tree-based index structure for high dimensional multimedia data

  • Yoon-Sik Tak
  • Seungmin Rho
  • Eenjun Hwang
  • Hanku Lee


Dominant features for the content-based image retrieval usually have high-dimensionality. So far, many researches have been done to index such values to support fast retrieval. Still, many existing indexing schemes are suffering from performance degradation due to the curse of dimensionality problem. As an alternative, heuristic algorithms have been proposed to calculate the answer with ‘high probability’ at the cost of accuracy. In this paper, we propose a new hash tree-based indexing structure called tertiary hash tree for indexing high-dimensional feature data. Tertiary hash tree provides several advantages compared to the traditional extendible hash structure in terms of resource usage and search performance. Through extensive experiments, we show that our proposed index structure achieves outstanding performance.


Tertiary hash tree Extendible hash Content-based Image retrieval Multi-dimensional data 



This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-C1090-1001-0008)


  1. 1.
    Beckmann N, Seeger B (2009) A revised r*-tree in comparison with related index structures. ACM SIGMOD Conf. on Management of Data, pp 799–812Google Scholar
  2. 2.
    Beckmann N, Kriegel H-P, Schneider R, Seeger B (1990) The R*-Tree: an Efficient and Robust Access Method for Points and Rectangles. Proc. of the ACM SIGMOD Conf. on Management of Data, pp 322–331Google Scholar
  3. 3.
    Berchtold S, Keim D, Kriegel H-P (1996) The X-tree: an index structure for high-dimensional data. Proc. of the Conf. on Very Large Databases, pp 28–39Google Scholar
  4. 4.
    Berchtold S, Boehm C, Jagadish HV, Kriegel HP, Sander J (2000) Independent quantization: an index compression technique for high-dimensional data spaces. ICDE, pp 577–588Google Scholar
  5. 5.
    Chakrabarti K, Mehrotra S (1999) The hybrid Tree: an index structure for high dimensional feature spaces. Proc. of the 15th Conf. on Data Engineering, pp 440–447Google Scholar
  6. 6.
    Fagin R, Nievergelt J, Pippenger N, Strong H (1979) Extendible hashing—a fast access method for dynamic files. ACM Trans Database Syst 4(3):315–344CrossRefGoogle Scholar
  7. 7.
    Faloutsos C, Lin K (1995) FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. Proc. of the ACM SIGMOD Conf. on Management of Data, pp 163–174Google Scholar
  8. 8.
    Guttman A (1984) R-trees: a dynamic index structure for spatial searching. Proc. of the ACM SIGMOD Conf. on Management of Data, pp 47–57Google Scholar
  9. 9.
    Ip Lin K, Jagadish HV, Faloutsos C (1994) The TV-tree: an index structure for high-dimensional data. VLDB Journal 3(4):517–542CrossRefGoogle Scholar
  10. 10.
    Katayama N, Satoh S (1997) The SR-Tree: an index structure for high dimensional nearest neighbor queries. Proc. of the ACM SIGMOD Conf. on Management of Data, pp 69–380Google Scholar
  11. 11.
    Lin S, Ozsu M, Oria V, Ng R (2001) An extendible hash for multi-precision similarity querying of image databases. Proc. of the Conf. on Very Large Data Bases, pp 221–230Google Scholar
  12. 12.
    Sellis T, Roussopoulos N, Faloutsos C (1987) The R+-Tree: a dynamic index for multi-dimensional objects. Proc. of the Conf. on Very Large Data BasesGoogle Scholar
  13. 13.
    Slaney M, Casey M (2008) Locality-sensitive hashing for finding nearest neighbors. IEEE Signal Process Mag 25(2):128–131CrossRefGoogle Scholar
  14. 14.
    Tak Y, Hwang E (2009) Indexing and matching scheme for recognizing 3D objects from single 2D image. IMSA, pp 60–67Google Scholar
  15. 15.
    Wang H, Perng C-S (2001) The S2-Tree: an index structure for subsequence matching of spatial objects. Proc. of Fifth Pacific-Asic Conf. on Knowledge Discovery and Data MiningGoogle Scholar
  16. 16.
    White DA, Jain R (1996) Similarity indexing with the SS-Tree. Proc. of the 12th Conf. on Data Engineering, pp 516–523Google Scholar
  17. 17.
    Yianilos, Peter N (1993) Data structures and algorithms for nearest neighbor search in general metric spaces. Proc. of the 4th ACM-SIAM Symposium on Discrete algorithms, pp 311–321Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Yoon-Sik Tak
    • 1
  • Seungmin Rho
    • 1
  • Eenjun Hwang
    • 1
  • Hanku Lee
    • 2
  1. 1.Department of Electrical EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Division of Internet & Multimedia EngineeringKonkuk UniversitySeoulRepublic of Korea

Personalised recommendations