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We introduce Regression Tree Fields (RTFs), a fully conditional random field model for image labeling problems. RTFs gain their expressive power from the use of nonparametric regression trees that specify a tractable Gaussian random field, thereby ensuring globally consistent predictions. Our approach improves on the recently introduced decision tree field (DTF) model  in three key ways: (i) RTFs have tractable test-time inference, making efficient optimal predictions feasible and orders ofdoi:10.1109/cvpr.2012.6247950 dblp:conf/cvpr/JancsaryNSR12 fatcat:ri2vdnamx5fglibkesqyor3gr4