  Unsupervised image segmentation aims at clustering the set of pixels of an
image into spatially homogeneous regions. We introduce here a class of Bayesian
nonparametric models to address this problem. These models are based on a
combination of a Potts-like spatial smoothness component and a prior on
partitions which is used to control both the number and size of clusters. This
class of models is flexible enough to include the standard Potts model and the
more recent Potts-Dirichlet Process model \cite{Orbanz2008}. More importantly,
any prior on partitions can be introduced to control the global clustering
structure so that it is possible to penalize small or large clusters if
necessary. Bayesian computation is carried out using an original generalized
Swendsen-Wang algorithm. Experiments demonstrate that our method is competitive
in terms of RAND\ index compared to popular image segmentation methods, such as
mean-shift, and recent alternative Bayesian nonparametric models.
