Dimensionality reduction of multi-scale feature spaces using a separability criterion

An algorithm for classification task dependent multiscale feature extraction is suggested. The algorithm focuses on dimensionality reduction of the feature space subject to maximum preservation of classification information. It has been shown that, for classification tasks, class separability based features are appropriate alternatives to features selected based on energy and entropy criteria. Application of this idea to feature extraction from multi-scale wavelet packets is presented. At each level of decomposition an optimal linear transform that preserves class separabilities and results in a reduced dimensional feature space is obtained. Classification and feature extraction is performed at each scale and resulting “soft decisions” are integrated across scales. The suggested scheme can also be applied to other orthogonal or non-orthogonal multiscale transforms e.g. local cosine transform or Gabor transform. The suggested algorithm has been tested on classification and segmentation of some radar target signatures as well as textured and document images