Wavelet transform-based locally orderless images for texture segmentation

Texture analysis is an important issue in many areas like object recognition, image retrieval study, medical imaging, robotics, and remote sensing. Despite the development of a family of techniques over the last couple of decades, there are only a few reliable methods. Multiresolution techniques seem to be attractive for many applications. In this study, we present an approach based on the discrete wavelet transform and scale space concept. We integrate the framework of locally orderless images (LOIs) with the transform coefficients to obtain a flexible method for texture segmentation. Compared to intensity (spatial domain), the wavelet coefficients appear to be more reliable with respect to noise immunity and the ease of feature formation. Hence, we represent each discrete coefficient value with a probability density function to form isophote images. Each isophote image is then convolved with a Gaussian to form LOIs, which specify a local histogram in each transform point. These LOIs, or statistical moments computed from LOIs, can be regarded as texture features. An experiment with the standard Brodatz's and VisTex texture databases demonstrates the superior performance of the wavelet-based LOIs compared to conventional LOI-based moments or wavelet and Gabor energy features. The elegance of the approach is in the relatively greater flexibility in producing segmentation results. A simple minimum distance classifier and confusion matrix analyses confirm the above attributes. (C) 2003 Elsevier B.V. All rights reserved.