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This paper reports on tree-based shape encoding and classification. We present an approach that combines characteristics from the theories of R-trees known from data base indexing and regression trees known from pattern recognition. The resulting shape representations are highly storage efficient. As they immediately transform into scale invariant signatures, we apply the Earth Mover's distance for computing shape similarities. Experimental results underline the efficacy of this approach. The required computations are simple and fast but allow for robust shape classification and clustering.