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Visual grouping is a key mechanism in human scene perception. There, itbelongs to the subconscious, early processing and is key prerequisite for otherhigh level tasks such as recognition. In this paper, we introduce an efficient,realtime capable algorithm which likewise agglomerates a valuable hierarchicalclustering of a scene, while using purely local appearance statistics. To speedup the processing, first we subdivide the image into meaningful, atomicsegments using a fast Watershed transform. Starting from there, our rapid,agglomerative clustering algorithm prunes and maintains the connectivity graphbetween clusters to contain only such pairs, which directly touch in the imagedomain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. Thecore of this approach is our novel cluster distance: it combines boundary andsurface statistics both in terms of appearance as well as spatial linkage. Thisyields state-of-the-art performance, as we demonstrate in conclusiveexperiments conducted on BSDS500 and Pascal-Context datasets.

Realtime Hierarchical Clustering based on Boundary and Surface Statistics