Abstract
Scientists are collecting data from observations and simulations at an ever increasing pace. In order to extract useful information from these massive datasets, they are turning to data mining techniques as an attractive solution approach. Data mining is an iterative and interactive process that consists of data pre-processing and pattern recognition. Pre-processing the raw data in order to transform it into a form suitable for pattern recognition is an important and timeconsuming first step. In this paper, we discuss the crucial role multiresolution techniques can play in the pre-processing of massive datasets. Using both simulated and real images, we describe our work in de-noising image data using wavelet-based multiresolution techniques. Our initial experiences show that a judicious choice of wavelet transforms, threshold selection methods, and threshold application schemes can effectively reduce the noise in the data without a significant loss of the signal.
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References
Anant, K., Dowla, F., Rodrigue, G.: Detection of the electrocardiogram P-wave using wavelet analysis. Proc. of the SPIE 2242 (1994) 744–749.
Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31 (1983) 532–540.
Chan, T., Zhou, H.: Adaptive ENO-wavelet transforms for discontinuous functions. UCLA Technical Report 99-21 (1999) June.
Chan, T., Zhou, H.: Optimal construction of wavelet coefficients using total variation regularization in image compression. UCLA Technical Report CAM 00-27 (2000) July.
Daubechies, I.: Orthonormal bases of compactly supported wavelets. Commun. on Pure and Applied Mathematics 41 (1988) 909–996.
Daubechies, I.: Ten Lectures on Wavelets. SIAM (1992).
Donoho, D. L., Johnstone, I. M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81 (1994) 425–455.
Donoho, D. L., Johnstone, I. M.: Adapting to unknown smoothness via wavelet shrinkage. JASA 90 (1995) 1200–1224.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM Special Issue on Data Mining 39 (1996) 27–34.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge, Mass. (1996).
FBI Fingerprint Image Compression Standard Web Page: http://www.c3.lanl.gov/~brislawn/FBI/FBI.html.
Fodor, I. K., Cantü-Paz, E., Kamath, C., Tang, N. A.: Finding bent-double radio galaxies: a case study in data mining. Computing Science and Statistics 23 (2000).
Fodor, I. K., Kamath, C: On de-noising images using wavelet-based statistical techniques. Manuscript in preparation (2001).
JPEG 2000 Standard Web Page: http://www.jpeg.org.
LeMoigne, J.: Parallel registration of multi-sensor remotely sensed imagery using wavelet coefficients. Proc. SPIE Wavelet Applications Conference (1994) 432–443.
Li, H., Manjunath, B. S., Mitra, S. K.: Multisensor image fusion using the wavelet transform. Proc. First International Conference on Image Processing (1994) 51–55.
Ma, W. Y., Manjunath, B. S.: A comparison of wavelet transform features for texture image annotation. Proc. Second International Conference on Image Processing (1995) 256–259.
Malladi, R., Sethian, J.: A unified approach to noise removal, image enhancement, and shape recovery. IEEE Trans. Image Processing 5 (1996) 1154–1168.
Mallat, S.: Multiresolution approximation and wavelet orthonormal bases of L2. Trans. American Mathematical Society 315 (1989) 69–87.
Meng, Q., Thompson, W., Flachs, G., Jordan, J.: Wavelet transform application in human face recognition. Proc. of the SPIE 3068 (1997) 124–135.
MPEG-4 Standard Web Page: http://www.cselt.it/mpeg/.
Ogden, R. T.: Essential Wavelets for Statistical Applications and Data Analysis. Birkhäuser (1997).
Sapphire Web Page: Sapphire: Large-scale Data Mining and Pattern Recognition. http://www.llnl.gov/casc/sapphire.
Starck, J.-L., Murtagh, F., Bijaoui, A.: Image and Data Analysis: The Multiscale Approach. Cambridge University Press (1998).
Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley-Cambridge Press (1996).
Uhl, A.: Wavelets: adaptive and parallel methods in image coding and signal processing. PhD thesis, University of Salzburg (1996).
Umbaugh, S.: Computer Vision and Image Processing: A Practical Approach using CVIPtools. Prentice Hall (1998).
Unser M., Aldroubi, A.: A review of wavelets in biomédical applications. Proc. of the IEEE 84 (1996) 626–638.
Vidakovic, B.: Statistical Modeling by Wavelets. Wiley Series in Probability and Statistics. John Wiley & Sons (1999).
Weeks, A.: Fundamentals of Electronic Image Processing. SPIE Optical Engineering Press and IEEE Press (1996).
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Fodor, I.K., Kamath, C. (2002). The Role of Multiresolution in Mining Massive Image Datasets. In: Barth, T.J., Chan, T., Haimes, R. (eds) Multiscale and Multiresolution Methods. Lecture Notes in Computational Science and Engineering, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56205-1_8
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DOI: https://doi.org/10.1007/978-3-642-56205-1_8
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