Subset selection using rough set in wavelet packet based texture classification

Rough set based attribute significance measure and reduction is proposed in this paper, after we decompose textures using wavelet packet and extract the l₁-norm as features, condition attributes are discretized with equal width binning method. We deduce the classification rules with the selected feature subset. The classification performance is tested on a set of 13 Brodatz texture, the averaged classification results show that the proposed algorithm can get rid of redundancy and only a few of the features can fulfill the classification task without reducing accuracy.