Abstract
Wavelet-based signal classification and compression techniques have gained substantial popularity due to their low computational costs, excellent localization properties, and the ability to choose from a large number of bases for signal representation. A difficulty with applying wavelet-based techniques is determining the best wavelet type and basis. For compression, the best-basis algorithm has been proposed to find the wavelet-packet basis with maximum energy compaction. The local discriminant basis (LDB) was previously introduced in the context of signal classification to find the best wavelet-packet basis with maximum class discrimination. In this paper, we apply the LDB to the problem of automatic target recognition. We describe the mechanics of the LDB as applied to one- and two-dimensional signals, and introduce a novel classification scheme that is tolerant to image scaling and moderate image translations and dilations.
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