  Visual grouping is a key mechanism in human scene perception. There, it
belongs to the subconscious, early processing and is key prerequisite for other
high level tasks such as recognition. In this paper, we introduce an efficient,
realtime capable algorithm which likewise agglomerates a valuable hierarchical
clustering of a scene, while using purely local appearance statistics. To speed
up the processing, first we subdivide the image into meaningful, atomic
segments using a fast Watershed transform. Starting from there, our rapid,
agglomerative clustering algorithm prunes and maintains the connectivity graph
between clusters to contain only such pairs, which directly touch in the image
domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The
core of this approach is our novel cluster distance: it combines boundary and
surface statistics both in terms of appearance as well as spatial linkage. This
yields state-of-the-art performance, as we demonstrate in conclusive
experiments conducted on BSDS500 and Pascal-Context datasets.
