A clustering approach using a time-frequency entropy measure of wavelet transform coefficients
| Autor: | R.L. Kirlin, R.M. Dizaji, B. Kaufhold |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Discrete wavelet transform
business.industry Second-generation wavelet transform Stationary wavelet transform Wavelet transform Cascade algorithm Pattern recognition Data_CODINGANDINFORMATIONTHEORY Wavelet packet decomposition Wavelet Artificial intelligence business Harmonic wavelet transform Mathematics |
| Zdroj: | IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). |
| DOI: | 10.1109/igarss.1998.703644 |
| Popis: | A non-parametric clustering approach that uses a time-frequency (TF) entropy measure taken from the signal wavelet transform coefficients is introduced (TFEWT). The TFEWT feature vector represents a concatenation of two vectors obtained from the projection of the signal wavelet entropy in TF space onto both the time and frequency axes. A signal-to-noise ratio criterion is evaluated to obtain the best clustering result by changing the signal time-frequency decomposition both by the basis set and the wavelet type. In comparison with FFT and different well known TF features like wavelet or wavelet packet coefficients, TFEWT renders a compact feature vector that optimizes the clustering criterion for distinct transient clusters, even when they have very similar TF energy distributions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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