Results using random field models for the segmentation of color images of natural scenes

We present results using a Markov random field color texture model for the unsupervised segmentation of images of outdoor scenes. The color random field model describes textured regions in terms of spatial interaction within color bands and between different color bands. The model is used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of the clustering is a step wise optimal merging process that at each iteration maximizes a global performance functional. The test for stopping the clustering is based on changes in the likelihood of the image. We provide experimental results that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation