Wold features for unsupervised texture segmentation

Chun-Shien Lu, Pau-Choo Chung

Abstract: An efficient texture representation for unsupervised segmentation is addressed based on the concept of Wold decomposition. Textures are described by the wavelet tuned to various scales and rotations to describe its deterministic component, and by the autoregressive model to describe its indeterministic component. The wavelet features and the AR parameters capturing the perceptual properties, "periodicity", "directionality", and "randomness", respectively, have been proved to be consistent with human texture perception. The performance of our approach is demonstrated on Brodatz textures and natural textured images.