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Proceedings Paper

Relations among wavelet coefficients and features for ATR
Author(s): Mieczyslaw M. Kokar; Marek K. Malczewski
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Paper Abstract

Wavelets have been used successfully for signal compression. A signal can be represented very concisely and with a high fidelity, by a set of wavelet coefficients. This suggests that wavelet coefficients can efficiently represent the contents of a signal and, consequently, could be used as features. Such features then can be used for signal classification. The quality of classification depends on the choice of the features. Fixing the set of features in both time and frequency domains results in the lack of invariance of the classification method with respect to translations and scaling of signals. In this paper we propose an approach that addresses this problem. We achieve this goal by using the following two techniques. First, our classification method test weather a specific relation among wavelet coefficients is satisfied by a given signal. And second, our method selects features dynamically, i.e., it searches for features that satisfy the relation. The relations are learned from a database of pre-classified signals. In this paper we provide the description of the relation learning approach and results of testing the approach on a simple scenario. The results of our simulations showed that this approach gives a higher classification accuracy than a similar approach based on a fixed set of features.

Paper Details

Date Published: 22 March 2001
PDF: 11 pages
Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421112
Show Author Affiliations
Mieczyslaw M. Kokar, Northeastern Univ. (United States)
Marek K. Malczewski, Northeastern Univ. (United States)


Published in SPIE Proceedings Vol. 4385:
Sensor Fusion: Architectures, Algorithms, and Applications V
Belur V. Dasarathy, Editor(s)

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