We present an unsupervised hierarchical segmentation algorithm for detecting complex heterogeneous image structures that are comprised of simpler homogeneous primitive objects. The first step segments primitive objects with uniform spectral content. Then, the co-occurrence information between neighboring regions is modeled and clustered. We assume that dense clusters of this co-occurrence space can be considered significant. Finally, the neighboring regions within these clusters are merged to obtain the next level in the segmentation hierarchy. The experiments show that the algorithm that iteratively clusters and merges region groups is able to segment heterogeneous structures in a hierarchical manner. © 2011 IEEE.