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Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
January 1999 (vol. 21 no. 1)
pp. 83-88

Abstract—In this paper, a new efficient feature extraction method based on the fast wavelet transform is presented. This paper especially deals with the assessment of process parameters or states in a given application using the features extracted from the wavelet coefficients of measured process signals. Since the parameter assessment using all wavelet coefficients will often turn out to be tedious or leads to inaccurate results, a preprocessing routine that computes robust features correlated to the process parameters of interest is highly desirable. The method presented divides the matrix of computed wavelet coefficients into clusters equal to rowvectors. The rows that represent important frequency ranges (for signal interpretation) have a larger number of clusters than the rows that represent less important frequency ranges. The features of a process signal are eventually calculated by the euclidean norms of the clusters. The effectiveness of this new method has been verified on a flank wear estimation problem in turning processes and on a problem of recognizing different kinds of lung sounds for diagnosis of pulmonary diseases.

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Index Terms:
Feature extraction, fast wavelet transform, signal interpretation.
Citation:
Stefan Pittner, Sagar V. Kamarthi, "Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 1, pp. 83-88, Jan. 1999, doi:10.1109/34.745739
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