Rotational invariant texture segmentation using directional wavelet-based fractal dimensions

Abstract

In this paper we introduce a feature set for texture segmentation, based on an extension of fractal dimension features. Fractal dimension extracts roughness information from images considering all available scales at once. In this work a single scale is considered at a time so that textures that do not possess scale invariance are sufficiently characterized. Single scale features are combined with multiple scale features for a more complete textural representation. Wavelets are employed for the computation of single and multiple scale roughness features due to their ability to extract information at different resolutions. Features are extracted at multiple directions using directional wavelets, and the feature vector is finally transformed to a rotational invariant feature vector that retains the texture directional information. An iterative K-means scheme is used for segmentation. The use of the roughness feature set results in high quality segmentation performance. The feature set retains the important properties of fractal dimension based features, namely insensitivity to absolute illumination and contrast.