Absrruct-This paper describes an approach to unsupervised segmentation of textured images. Local texture properties are extracted using local linear transforms that have been optimized for maximal texture discrimination. Local statistics (texture energy measures) are estimated at the output of an equivalent filter bank by means of a nonlinear transformation (absolute value) followed by an iterative Gaussian smoothing algorithm. This procedure generates a multiresolution sequence of feature planes with a half octave scale progression. A new feature reduction technique is then applied to the data and is determined bj simultaneouslj diagonalizing scatter matrices evaluated at t\+o different spatial resolutions. This approach provides a good approximation of Fisher's multiple linear discriminants and has the advantage of requiring no upriori knowledge. This new feature reduction methods appears to be an improvement on the commonly used Karhunen-Loeve transform and allows efficient texture segmentation based on simple thresholding. lndex Terms-Feature reduction, filter bank, Karhunen-Loeve transform, local linear transform, (multiple or Fisher's) discriminants, segmentation. texture. threshold.
One example of these techniques is the one proposed by REF , that extracts local texture properties using local linear transforms that were optimized for maximal texture discrimination.
Multiresolution feature extraction and selection for texture segmentation
IEEE Trans. Pattern Anal. Mach. Intell.
IEEE Trans. Pattern Anal. Mach. Intell.
Computer Science