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A Multiwavelet Based Spatial Image Processing and its Application to Adaptive Data Mining

Md Ateeq Ur Rahman, Shaik Rusthum

Abstract


This paper contributes towards the development of adaptive learning system for automated segmentation and prediction of isolated regions in given spatial images. The effect of spatial distortion is observed in the spatial images under different processing noise conditions. A method for image denoising, shape and textural feature information using multi wavelet transformation is suggested. The regions in the image are estimated using global graph theory technique. A methodology to provide guidance for mining remote sensing image data is proposed. To improve the accuracy of estimation, hierarchal clustering over distributed data sample is presented. The concepts of linear relation among various clusters are explored and are incorporated in data mining approach. The performance of retrieval time and classification accuracy has been evaluated for various cases.

Keywords


Clustering, Denoising, Representative Features, Wavelet Transformation.

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References


J. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. on Image Processing, vol. 11, 2002, pp. 670–684.

Jiawei Han, Yandong Cai, and Nick Cercone, “Knowledge Discovery in Databases: An Attribute-Oriented Approach” Proceedings of the 18th VLDB Conference Vancouver, 1992, Canada.

Aksoy, S., et al., “Interactive Training of Advanced Classifiers for Mining Remote Sensing Image Archives”. In ACM International Conference on Knowledge Discovery and Data Mining. 2004. Seattle.

Rushing, J., et al., “ADaM: A Data Mining Toolkit for Scientists and Engineers”. Computers and Geosciences, In Press, Available online 11 January 2005, 2005.

Javier Portilla, Vasily Strela, Martin J. Wainwright, and Eero P. Simoncelli, “Image Denoising Using Scale Mixtures of Gaussians in the Wavelet Domain” IEEE Trans on Image Processing, vol.12, no.11, 2003, pp. 1338- 1350.

Tao Li, Qi Li, Shenghuo Zhu, Mitsunori Ogihara, “A survey on wavelet applications in data mining”, ACM SIGKDD, Vol 4, No 2, 2002, pp.49 – 68.

Chambolle, R. A. DeVore, N. Lee, and B. J. Lucier, “Nonlinear wavelet image processing: Variational problems, compression, and noise removal through wavelet shrinkage,” IEEE Trans. on Image Processing, vol. 7, 1998, pp. 319–335.

http://www.gisdevelopment.net/tutorials/tuman005.htm.(Accessed on 02-10-2010)

S. Grace Chang, Bin Yu and Martin Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising” IEEE Trans. on Image Processing, vol. 9, 2000, pp.1522–1531.

Yuri Y. Boykov Marie-Pierre Jolly, “Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images”. Proceedings of Internation Conference on Computer Vision, Vancouver, Canada, vol.I, 2001, pp.105-112

Jitendra Malik, Serge Belongie, Thomas Leung et al., “Contour and Texture Analysis for Image Segmentation” International Journal of Computer Vision 43(1), 2001, pp. 7–27.

Schroder, M., et al., “Interactive Learning and Probabilistic Retrieval in Remote Sensing Image Archives”. IEEE Trans. on Geoscience and Remote Sensing. Vol 23(9), 2000, pp. 2288-2298.

Curlander, J., Kober, W., “Rule based system for thematic classification in SAR imagery”. Proc. IGARSS. IEEE Press, New York, 1992, pp. 854– 856.

Peter Howarth and Stefan Ruger, P. Enser et al. (Eds.), “Evaluation of Texture Features for Content-Based Image Retrieval” CIVR, LNCS 2004, Springer Berlin, pp. 326–334.

Tsatsoulis, C., “Expert systems in remote sensing applications”. IEEE Geoscience and Remote Sensing Newsletter, June 1993, pp. 7 –15.

C. Ding, X. He, H. Zha, M. Gu, and H. Simon, “A min-max cut algorithm for graph partitioning and data clustering”. Proc. IEEE Intl Conf. Data Mining, 2001, pp.107–114.

Chris Ding, Xiaofeng He, "Cluster merging and splitting in hierarchical clustering algorithms", Second IEEE International Conference on Data Mining. 2002, pp.139-148.

Heene, G., Gautama, S., “Optimisation of a coastline extraction algorithm for object-oriented matching of multisensor satellite Imagery”. Proc. IGARSS, IEEE Press, vol. 6, New York, 2000, pp. 2632–2634.

S. Loncaric, “A survey of shape analysis techniques,” Pattern Recognition, Vol. 31, No. 8, 1998. pp. 983–1001.

R. C.Veltkamp and M. Hagedoorn, “State of the art in shape matching,”Utrecht University, The Netherlands, Tech. Rep, 1999, UU-CS-1999-27.

D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognition Elsevier, Volume 37, Issue 1, January 2004, Pages 1-19.

Unser, M., “Texture classification and segmentation using wavelet frames”. IEEE Trans. Image Processing, Vol 4 No. 11, 1995, pp. 1549-1560.

Aujol, J., Aubert, G., Blanc-Feraud, L., “Wavelet-based level set evolution for classification of textured images”. IEEE Trans. Image Processing, Vol 12 No 12, 2003, pp.1634-1641.


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