IMET: Image Mining For Typhoon Analysis

  • Kitamoto Asanobu
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 22)


Visual information management systems (VIMS) have been expanding its application to new domains where multimedia information is relevant. We believe that the issue we discuss in this chapter, typhoon data mining, is one of new application domains that VIMS should play a vital role. The target of our research is the large collection of typhoon images, which consists of approximately 34,000 well-framed images created from meteorological geostationary satellite images. In this chapter, this image collection is thoroughly examined by means of various data mining approaches, such as principal component analysis, K-means clustering, self-organizing map and wavelet transform, with the aim of discovering regularities and anomalies hidden in the typhoon cloud patterns. Here the consistent quality of the typhoon image collection makes such large-scale image data mining feasible, but the spatio-temporal complexity of the typhoon image collection poses serious challenges to the informatics community as a large-scale real world application.


Tropical Cyclone Query Language Recurrence Plot Data Mining Approach Nonlinear Time Series Analysis 
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.


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  1. Blake, A. and Isard, M. (1998). Active Contours. Springer.Google Scholar
  2. Duda, R. and Hart, P. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons.Google Scholar
  3. Dvorak, V. (1984). Tropical cyclone intensity analysis using satellite data. NOAA Technical Report NESDIS, 11:1–47.Google Scholar
  4. Fayyad, U., Smyth, P., Weir, N., and Djorgovski, S. (1995). Automated analysis and exploration of image databases: Results, progress, and challenges. J. Intell. Info. Syst., 4:7–25.CrossRefGoogle Scholar
  5. Girolami, M., editor (2000). Advances in Independent Component Analysis. Springer.Google Scholar
  6. Kantz, H. and Schreiber, T. (1997). Nonlinear Time Series Analysis. Cambridge University Press.Google Scholar
  7. Kitamoto, A. (2000). The development of typhoon image database with content-based search. In Proc. 1st Int. Symp. Adv. Informatics, pages 163–170.Google Scholar
  8. Kitamoto, A. (2001). Data mining for typhoon image collection. In 2nd Int. Workshop on Multimedia Data Mining, pages 68–77.Google Scholar
  9. Kitamoto, A. and Ono, K. (2001). The collection of typhoon image data and the establishment of typhoon information databases under international research collaboration between Japan and Thailand. NII Journal, (2): 15–26.Google Scholar
  10. Kohonen, T. (1997). Self-Organizing Maps. Springer, second edition.Google Scholar
  11. Langley, P. (1996). Elements of Machine Learning. Morgan Kaufmann Publishers, Inc.Google Scholar
  12. Lee, D. D. and Seung, H. S. (1999). Learning the parts of objects by non-negative matrixGoogle Scholar
  13. Lee, R. and Liu, J. (1999). An automatic satellite interpretation of tropical cyclone patterns using elastic graph dynamic link model. Patt. Recog. Art. Intell., 13(8): 1251–1270.CrossRefGoogle Scholar
  14. Lorenz, E. (1969a). Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26:636 –646.CrossRefGoogle Scholar
  15. Lorenz, E. (1969b). Three approaches to atmospheric predictability. Bul. Amer. Meteo. Soc., 50(5):345–349.Google Scholar
  16. Müller, W., Pecenovic, Z., de Vries, A., Squire, D., Müller, H., and Pun, T. (2000). MRML: Towards an extensible standard for multimedia querying and benchmarking. Technical report, Computing Science Center, University of Geneva.Google Scholar
  17. Smeulders, A., Worring, M., Santini, S., Gupta, A., and Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Trans. Patt. Anal. Mach. Intell., 22(12):1349–1380.CrossRefGoogle Scholar
  18. Starck, J., Murtagh, F., and Bijaoui, A. (1998). Image Processing and Data Analysis: the Multiscale Approach. Cambridge University Press.Google Scholar
  19. Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. J. of Cognitive Neuroscience, 3(1):71–86.CrossRefGoogle Scholar
  20. Wilks, D. (1995). Statistical Methods in the Atmospheric Sciences. Academic Press.Google Scholar
  21. Zehr, R. (2000). Tropical cyclone research using large infrared image datasets. In 24th Conf. Hurricanes and Tropical Meteo., pages 486–487. American Meteorological Society.Google Scholar
  22. Zhou, L., Kambhamettu, C., and Goldgof, D. (2000). Fluid structure and motion analysis from multi-spectrum 2D cloud image sequences. In Proc. of IEEE Conf. CVPR. Vol. II, pages 744–751.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Kitamoto Asanobu
    • 1
  1. 1.National Institute of InformaticsJapan

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