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


Affiliations
1 Department of Computer Science & Engineering, Shadan College of Engineering & Technology, Hyderabad, India
2 Brilliant Institute of Engineering & Technology, Hyderabad, India
     

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

Abstract Views: 193  |  PDF Views: 1

Authors

Md. Ateeq Ur Rahman
Department of Computer Science & Engineering, Shadan College of Engineering & Technology, Hyderabad, India
Shaik Rusthum
Brilliant Institute of Engineering & Technology, Hyderabad, India

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.