Intelligent Mining in Image Databases, with Applications to Satellite Imaging and to Web Search
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.
KeywordsFast Fourier Transform Optimality Criterion Inverse Fourier Transform String Match Simple Image
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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