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Intelligent Mining in Image Databases, with Applications to Satellite Imaging and to Web Search

  • Stephen Gibson
  • Vladik Kreinovich
  • Luc Longpre
  • Brian Penn
  • Scott A. Starks
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 68)

Abstract

An important part of our knowledge is in the form of images. For example, a large amount of geophysical and environmental data comes from satellite photos, a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in data mining. Unfortunately, most existing data mining techniques have been designed for mining numerical data and are thus not well suited for image databases. Hence, new methods are needed for image mining. In this paper, we show how data mining can be used to find common patterns in several images.

Keywords

Fast Fourier Transform Optimality Criterion Inverse Fourier Transform String Match Simple Image 
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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References

  1. [1]
    H. Bunke and M. Zumbuehl, “Acquisition of 2D shape models from scenes with overlapping objects using string matching”, Pattern Anal. Appl., 1999, Vol. 2, No. 1, pp. 2–9.CrossRefMATHGoogle Scholar
  2. [2]
    K. J. Cios, W. Pedrycz, and R. Swiniarski, Data Mining Methods for Knowledge Discovery, Kluwer. Dordrecht, 1998.CrossRefMATHGoogle Scholar
  3. [3]
    U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA, 1996.Google Scholar
  4. [4]
    S. Gibson, An optimal FFT-based algorithm for mosaicing images, Master Thesis, Department of Computer Science, University of Texas at El Paso, December 1999.Google Scholar
  5. [5]
    X. Jiang, K. Yu, and H. Bunke, “Detection of rotational and involutional symmetries and congruity of polyhedra”, Visual Comput., 1996, Vol. 12, No. 4, pp. 193–201.MATHGoogle Scholar
  6. [6]
    L. T. Koczy, V. Kreinovich, Y. Mendoza, H. T. Nguyen, and H. Schulte, “Towards Mathematical Foundations of Information Retrieval: Dependence of Websité s Relevance on the Number of Occurrences of a Queried Word”, Proceedings of the Joint Conferences in Information Sciences JCIS’2000}, Atlantic City, NJ, February 27-March 3, 2000 (to appear).Google Scholar
  7. [7]
    O. Kosheleva, L. Longpre, and R. Osegueda, “Detecting Known Non-Smooth Structures in Images: Fuzzy and Probabilistic Methods, with Applications to Medical Imaging, Non-Destructive Testing, and Detecting Text on Web Pages”, Proceedings of The Eighth International Fuzzy Systems Association World Congress IFSA’99, Taipei, Taiwan, August 1720, 1999, pp. 269–273.Google Scholar
  8. [8]
    V. Kreinovich, C. Quintana, and O. Fuentes. “Genetic algorithms: what fitness scaling is optimal?” Cybernetics and Systems: an International Journal, 1993, Vol. 24, No. 1, pp. 9–26.MathSciNetCrossRefMATHGoogle Scholar
  9. [9]
    J. Llados, H. Bunke, and E. Marti, “Finding rotational symmetries by cyclic string matching”, Pattern Recognit. Lett., 1997, Vol. 18, No. 14, pp. 1435–1442.MATHGoogle Scholar
  10. [10]
    R. S. Michalski, M. Kubat, I. Bratko, and A. Bratko (eds.), Machine Learning and Data Mining: Methods and Applications, J. Wiley & Sons, New York, 1998.Google Scholar
  11. [11]
    H. T. Nguyen and V. Kreinovich, Applications of continuous mathematics to computer science, Kluwer, Dordrecht, 1997.MATHGoogle Scholar
  12. [12]
    L. Polkowski et al. (eds.), Rough sets in knowledge discovery 1. Methodology and applications, Physica-Verlag: Heidelberg, 1998 (Studies in Fuzziness and Soft Comput. Vol. 18).Google Scholar
  13. [13]
    B. S. Reddy and B. N. Chatterji, “An 11-.1-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration,” IEEE Transactions on Image Processing, 1996, Vol. 5, No. 8, pp. 1266–1271.CrossRefGoogle Scholar
  14. [14]
    K. Shearer, H. Bunke, S. Venkatesh, and D. Kieronska, “Efficient graph matching for video indexing”, in: J.-M. Jolion et al. (eds.), Graph based representations in pattern recognition. Workshop, GbR ’97, Lyon, France, April 17–18, 1997, Wien: Springer: Wien, Comput. Suppl. 1998, Vol. 12, pp. 53–62.Google Scholar
  15. [15]
    Y.-Q. Zhang and A. Kandel, Compensatory Genetic Fuzzy Neural Networks and Their Applications, World Scientific, Singapore, 1998.MATHGoogle Scholar
  16. [16]
    N. Zhong, A. Skowron, and S. Ohsuga (eds.), New directions in rough sets, data mining, and granular-soft computing, Proc. of the 7th international workshop, RSFDGrC ’99, Yamaguchi, Japan, November 9–11, 1999. Proceedings, Springer-Verlag Lecture Notes in Artificial Intelligence, Vol. 1711, Berlin, 1999Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Stephen Gibson
    • 1
    • 2
  • Vladik Kreinovich
    • 1
    • 2
  • Luc Longpre
    • 1
  • Brian Penn
    • 2
  • Scott A. Starks
    • 2
  1. 1.Department of Computer ScienceUniversity of Texas at El Paso 500 W. UniversityEl PasoUSA
  2. 2.NASA Pan-American Center for Earth and Environmental Sciences (PACES)University of Texas at El Paso 500 W. UniversityEl PasoUSA

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