A multi-resolution feature reduction technique for image segmentation with multiple components

The authors present a linear feature-reduction technique for multicomponent or textured image segmentation. The transformation matrix is computed by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions. Under reasonable conditions, this transform closely approximates the generalized Fisher linear discriminants which are optimal for region separability. Experimental examples suggest that this technique is superior to the Karhunen-Loeve transform for texture segmentation