Miguel A. Veganzones, Mihai Datcu, Manuel Graña


The normalized information distance (NID) is an universal metric distance based on Kolmogorov complexity. However, NID is not computable in a Turing sense. The normalized compression distance (NCD) is a computable distance that approximates NID by using normal compressors. NCD is a parameter-free distance that compares two signals by their lengths after separate compression relative to the length of the signal resulting from their concatenation after compression. The use of NCD for image retrieval over large image databases is difficult due to the computational cost of compressing the query image concatenated with every image in the database. The use of dictionaries extracted by dictionary-based compressors, such as the LZW compression algorithm, has been proposed to overcome this problem. Here we propose a Content-Based Image Retrieval system based on such dictionaries for the mining of hyperspectral databases. We compare results using the Normalized Dictionary Distance (NDD) and the Fast Dictionary Distance (FDD) against the NCD over different datasets of hyperspectral images. Results validate the applicability of dictionaries for hyperspectral image retrieval.


  1. Bennett, C., Gacs, P., Li, M., Vitanyi, P. M., and Zurek, W. (1998). Information distance. Information Theory, IEEE Transactions on, 44(4):1407-1423.
  2. Cerra, D. and Datcu, M. (2010). Image retrieval using compression-based techniques. In 2010 International ITG Conference on Source and Channel Coding (SCC), pages 1-6. IEEE.
  3. Cerra, D., Mallet, A., Gueguen, L., and Datcu, M. (2010). Algorithmic information Theory-Based analysis of earth observation images: An assessment. IEEE Geoscience and Remote Sensing Letters, 7(1):8-12.
  4. Chaitin, G. J. (2004). Algorithmic Information Theory. Cambridge University Press.
  5. Cilibrasi, R. and Vitanyi, P. (2005). Clustering by compression. Information Theory, IEEE Transactions on, 51(4):1523-1545.
  6. Daschiel, H. and Datcu, M. (2005). Information mining in remote sensing image archives: system evaluation. Geoscience and Remote Sensing, IEEE Transactions on, 43(1):188-199.
  7. Li, M., Chen, X., Li, X., Ma, B., and Vitanyi, P. (2004). The similarity metric. Information Theory, IEEE Transactions on, 50(12):3250-3264.
  8. Li, M. and Vitanyi, P. (1997). An Introduction to Kolmogorov Complexity and Its Applications. Springer, 2nd edition.
  9. Macedonas, A., Besiris, D., Economou, G., and Fotopoulos, S. (2008). Dictionary based color image retrieval. Journal of Visual Communication and Image Representation, 19(7):464-470.
  10. Muller, H., Muller, W., Squire, D. M., Marchand-Maillet, S., and Pun, T. (2001). Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recognition Letters, 22(5):593-601.
  11. Plaza, A., Plaza, J., Paz, A., and Blazquez, S. (2007). Parallel CBIR system for efficient hyperspectral image retrieval from heterogeneous networks of workstations. In International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2007. SYNASC, pages 285-291. IEEE.
  12. Shannon, C. E. (2001). A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev., 5(1):3-55.
  13. Smeulders, A., Worring, M., Santini, S., Gupta, A., and Jain, R. (2000). Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(12):1349- 1380.
  14. Solomonoff, R. J. (2009). Algorithmic probability: Theory and applications. In Information Theory and Statistical Learning, pages 1-23. Springer US, Boston, MA.
  15. Veganzones, M. A., Maldonado, J. O., and Grana, M. (2008). On Content-Based image retrieval systems for hyperspectral remote sensing images. In Computational Intelligence for Remote Sensing, volume 133 of Studies in Computational Intelligence, pages 125- 144. Springer Berlin / Heidelberg.
  16. Watanabe, T., Sugawara, K., and Sugihara, H. (2002). A new pattern representation scheme using data compression. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):579-590.

Paper Citation

in Harvard Style

A. Veganzones M., Datcu M. and Graña M. (2012). DICTIONARY BASED HYPERSPECTRAL IMAGE RETRIEVAL . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012) ISBN 978-989-8425-98-0, pages 426-432. DOI: 10.5220/0003861904260432

in Bibtex Style

author={Miguel A. Veganzones and Mihai Datcu and Manuel Graña},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012)},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: PRARSHIA, (ICPRAM 2012)
SN - 978-989-8425-98-0
AU - A. Veganzones M.
AU - Datcu M.
AU - Graña M.
PY - 2012
SP - 426
EP - 432
DO - 10.5220/0003861904260432