  In application domains such as robotics, it is useful to represent the
uncertainty related to the robot's belief about the state of its environment.
Algorithms that only yield a single "best guess" as a result are not
sufficient. In this paper, we propose object proposal generation based on
non-parametric Bayesian inference that allows quantification of the likelihood
of the proposals. We apply Markov chain Monte Carlo to draw samples of image
segmentations via the distance dependent Chinese restaurant process. Our method
achieves state-of-the-art performance on an indoor object discovery data set,
while additionally providing a likelihood term for each proposal. We show that
the likelihood term can effectively be used to rank proposals according to
their quality.
