Multidimensional Descriptor Indexing: Exploring the BitMatrix

  • Catalin Calistru
  • Cristina Ribeiro
  • Gabriel David
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


Multimedia retrieval brings new challenges, mainly derived from the mismatch between the level of the user interaction—high-level concepts, and that of the automatically processed descriptors—low-level features. The effective use of the low-level descriptors is therefore mandatory. Many data structures have been proposed for managing the representation of multidimensional descriptors, each geared toward efficiency in some set of basic operations. The paper introduces a highly parametrizable structure called the BitMatrix, along with its search algorithms. The BitMatrix is compared with existing methods, all implemented in a common framework . The tests have been performed on two datasets, with parameters covering significant ranges of values. The BitMatrix has proved to be a robust and flexible structure that can compete with other methods for multidimensional descriptor indexing.


Recall Rate Indexing Method Query Object Multimedia Object Query Signature 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mojsilovic, A.: Semantic Metric for Image Library Exploration. IEEE Transactions on Multimedia 6, 828–838 (2004)CrossRefGoogle Scholar
  2. 2.
    Martínez, J.M. (ed.): MPEG-7 Requirements Group: MPEG-7 Overview v.10. ISO/IEC JTC1/SC29/WG11 N6828 (2004) Google Scholar
  3. 3.
    Calistru, C., Ribeiro, C., David, G.: A flexible model for multimedia content structure and description: MetaMedia and its applications (in preparation, 2006)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 Comput. Surv. 33, 322–373 (2001)CrossRefGoogle Scholar
  5. 5.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Comput. Surv. 33, 273–321 (2001)CrossRefGoogle Scholar
  6. 6.
    Digout, C., Nascimento, M.A.: High-dimensional similarity searches using a metric pseudo-grid. In: ICDE Workshops 1174 (2005)Google Scholar
  7. 7.
    Jagadish, H.V., Ooi, B.C., Tan, K.L., Yu, C., Zhang, R.: iDistance: An adaptive B+-tree based indexing method for nearest neighbor search. ACM Trans. Database Syst. 30, 364–397 (2005)CrossRefGoogle Scholar
  8. 8.
    Carey, M.J., Haas, L.M., Schwarz, P.M., Arya, M., Cody, W.F., Fagin, R., Flickner, M., Luniewski, A.W., Niblack, W., Petkovic, D., Thomas, J., Williams, J.H., Wimmers, E.L.: Towards heterogeneous multimedia information systems: the Garlic approach. In: RIDE 1995: Proceedings of the 5th International Workshop on Research Issues in Data Engineering-Distributed Object Management (RIDE-DOM 1995), pp. 124–131. IEEE Computer Society, Los Alamitos (1995)CrossRefGoogle Scholar
  9. 9.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS 2001: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 102–113. ACM Press, New York (2001)CrossRefGoogle Scholar
  10. 10.
    Arjen, P.d.V., Mamoulis, N., Nes, N., Kersten, M.: Efficient k-NN search on vertically decomposed data. In: SIGMOD 2002: Proceedings of the 2002 ACM SIGMOD international conference on Management of data, pp. 322–333. ACM Press, New York (2002)Google Scholar
  11. 11.
    Weber, R., Schek, H.J., Blott, S.: A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. In: Proc. 24th Int. Conf. Very Large Data Bases, VLDB, pp. 194–205 (1998)Google Scholar
  12. 12.
    Aggarwal, C.C., Yu, P.S.: The IGrid index: Reversing the dimensionality curse for similarity indexing in high dimensional space. In: KDD 2000: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 119–129. ACM Press, New York (2000)CrossRefGoogle Scholar
  13. 13.
    Cha, G.H.: Bitmap indexing method for complex similarity queries with relevance feedback. In: MMDB 2003: Proceedings of the 1st ACM international workshop on Multimedia databases, pp. 55–62. ACM Press, New York (2003)CrossRefGoogle Scholar
  14. 14.
    Goldstein, J., Platt, J.C., Burges, C.J.C.: Redundant Bit Vectors for Quickly Searching High-Dimensional Regions. In: Deterministic and Statistical Methods in Machine Learning, pp. 137–158 (2004)Google Scholar
  15. 15.
    Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is Nearest Neighbor Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Aggarwal, C.C.: Towards meaningful high-dimensional nearest neighbor search by human-computer interaction. In: Proceedings of 18th International Conference on Data Engineering 2002, pp. 593–604 (2002)Google Scholar
  17. 17.
    Katayama, N., Satoh, S.: Distinctiveness-Sensitive Nearest Neighbor Search for Efficient Similarity Retrieval of Multimedia Information. In: Proceedings of the 17th International Conference on Data Engineering, pp. 493–502. IEEE Computer Society, Washington (2001)CrossRefGoogle Scholar
  18. 18.
    Gonçalves, B., Calistru, C., Ribeiro, C., David, G.: Experimental results for multidimensional multimedia descriptor indexing (2006) (Submitted for publication)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Catalin Calistru
    • 1
    • 2
  • Cristina Ribeiro
    • 1
    • 2
  • Gabriel David
    • 1
    • 2
  1. 1.FEUP—Faculdade de Engenharia da Universidade do Porto 
  2. 2.INESC—PortoPortoPortugal

Personalised recommendations