A fundamental challenge in analyzing spatial patterns in images is the notion of scale. Texture based analysis
typically characterizes spatial patterns only at the pixel level. Such small scale analysis usually fails to capture
spatial patterns that occur over larger scales. This paper presents a novel solution, termed hierarchical texture
motifs, to this texture-of-textures problem. Starting at the pixel level, spatial patterns are characterized using
parametric statistical models and unsupervised learning. Higher levels in the hierarchy use the same analysis to
characterize the motifs learned at the lower levels. This multi-level analysis is shown to outperform single-level
analysis in classifying a standard set of image textures.