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Relating Things and Stuff by High-Order Potential Modeling
Byung-soo Kim1, Min Sun1, Pushmeet Kohli2, and Silvio Savarese1
1University of Michigan, Ann Arbor, U.S.A
2Microsoft Research Cambridge, UK
Abstract. In the last few years, substantially different approaches have been adopted for segmenting and detecting “things” (object categories that have a well defined shape such as people and cars) and “stuff” (object categories which have an amorphous spatial extent such as grass and sky). This paper proposes a framework for scene understanding that relates both things and stuff by using a novel way of modeling high order potentials. This representation allows us to enforce labelling consistency between hypotheses of detected objects (things) and image segments (stuff) in a single graphical model. We show that an efficient graph-cut algorithm can be used to perform maximum a posteriori (MAP) inference in this model. We evaluate our method on the Stanford dataset [1] by comparing it against state-of-the-art methods for object segmentation and detection.
LNCS 7585, p. 293 ff.
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© Springer-Verlag Berlin Heidelberg 2012