Analysing micro- and macro-structures within images confers ability to include scale in texture analysis. Filtering allows for selection of texture structures at different scales, revealing the micro- and macro-structures which would otherwise be concealed. The new approach to texture segmentation uses low- and high-pass filters to achieve this scale-based analysis. Segmentation is performed using Local Binary Patterns as an example of the type of feature vector that can be used with the new process. These are generated for the original image and each of the filtered images. A two stage training process is used to learn the optimum filter sizes and to produce model histograms for each known texture class. These are used in the supervised segmentation of texture mosaics generated from the VisTex database. The results demonstrate the superiority of the new combined approach compared to the best multi-resolution LBP configuration and analysis only using low pass filters. Noise analysis has also confirmed the advantageous properties of low- and high-pass filtering, and confirms that it is optimal to combine the two forms in texture segmentation.