  The minimum cost lifted multicut approach has proven practically good
performance in a wide range of applications such as image decomposition, mesh
segmentation, multiple object tracking, and motion segmentation. It addresses
such problems in a graph-based model, where real-valued costs are assigned to
the edges between entities such that the minimum cut decomposes the graph into
an optimal number of segments. Driven by a probabilistic formulation of minimum
cost multicuts, we provide a measure for the uncertainties of the decisions
made during the optimization. We argue that access to such uncertainties is
crucial for many practical applications and conduct an evaluation by means of
sparsifications on three different, widely used datasets in the context of
image decomposition (BSDS-500) and motion segmentation (DAVIS2016 and FBMS59)
in terms of variation of information (VI) and Rand index (RI).
