Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blake, A. and Isard, M. (1998). Active Contours. Springer.
Duda, R. and Hart, P. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons.
Dvorak, V. (1984). Tropical cyclone intensity analysis using satellite data. NOAA Technical Report NESDIS, 11:1–47.
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.
Girolami, M., editor (2000). Advances in Independent Component Analysis. Springer.
Kantz, H. and Schreiber, T. (1997). Nonlinear Time Series Analysis. Cambridge University Press.
Kitamoto, A. (2000). The development of typhoon image database with content-based search. In Proc. 1st Int. Symp. Adv. Informatics, pages 163–170.
Kitamoto, A. (2001). Data mining for typhoon image collection. In 2nd Int. Workshop on Multimedia Data Mining, pages 68–77.
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.
Kohonen, T. (1997). Self-Organizing Maps. Springer, second edition.
Langley, P. (1996). Elements of Machine Learning. Morgan Kaufmann Publishers, Inc.
Lee, D. D. and Seung, H. S. (1999). Learning the parts of objects by non-negative matrix
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.
Lorenz, E. (1969a). Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26:636 –646.
Lorenz, E. (1969b). Three approaches to atmospheric predictability. Bul. Amer. Meteo. Soc., 50(5):345–349.
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.
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.
Starck, J., Murtagh, F., and Bijaoui, A. (1998). Image Processing and Data Analysis: the Multiscale Approach. Cambridge University Press.
Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. J. of Cognitive Neuroscience, 3(1):71–86.
Wilks, D. (1995). Statistical Methods in the Atmospheric Sciences. Academic Press.
Zehr, R. (2000). Tropical cyclone research using large infrared image datasets. In 24th Conf. Hurricanes and Tropical Meteo., pages 486–487. American Meteorological Society.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
Asanobu, K. (2003). IMET: Image Mining For Typhoon Analysis. In: Djeraba, C. (eds) Multimedia Mining. Multimedia Systems and Applications Series, vol 22. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1141-0_2
Download citation
DOI: https://doi.org/10.1007/978-1-4615-1141-0_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5412-3
Online ISBN: 978-1-4615-1141-0
eBook Packages: Springer Book Archive