A graph-theoretic approach to multiscale texture segmentation

We introduce a multiscale texture image segmentation algorithm based on wavelet packets and graphs. Wavelet packets are used to generate scale-space features with strong discriminating power. A graph-theoretic approach to clustering is employed starting at the coarsest scale and working to the finest. We begin with the construction of a fully connected, weighted, undirected graph among all coarse scale pixels. The weights are determined from the wavelet packet generated features. Segmentation is achieved by constructing a minimum spanning tree for this graph, which is subsequently partitioned into sub trees by breaking "inconsistent" edges. Each sub tree corresponds to a different texture. Segment information is recursively passed to the next finer scale where we introduce a novel approach to constructing complete graphs for only a subset of pixels thereby improving the speed of the method with negligible performance degradation. We demonstrate the performance of our algorithm on synthetic images as well as on high-resolution infrared imagery.