Nested partitions using texture segmentation

A multi-step method of partitioning the pixels of an image such that the partitions at one step are wholly nested inside the partitions of the next step is described, ie, we describe an agglomerative, hierarchical segmentation technique that uses texture information to perform the segmentation. The image is requantized using K-means clustering. Then, clusters are expanded using region growing and morphological processing. This provides the most detailed level of segmentation. The next coarser segmentation levels are obtained by steadily relaxing the inter-cluster distance between the clusters that is allowed by the morphological processing. Results are demonstrated on real-world images and swathes of Brodatz textures.