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Wavelet packet decomposition has been successfully applied to image analysis and classification. The most common approach for wavelet packet-based texture classification is to decompose texture images with wavelet packet transform and to extract energy values for all subbands as features for the subsequent classification. Due to the overcomplete representation provided by the wavelet packet transform,...
The regularized least square based discriminative projections (RLSDP) for extracting features was recently proposed, which aims to seek discriminant projection directions that maximize the between-class scatter and minimize the within-class compactness. However, in RLSDP, the retrieval samples are reconstructed by the coefficients only associated with the same class, and may have large errors. Moreover,...
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