Before revision:  Second, we demonstrate how to use this flexible design of the update rule to propagate segmentation information across layers of a deep network, and to train mixtures jointly across layers. Third, we compare the standard Gaussian mixture and the Student-t mixture, which is known to better capture the statistics of low-level visual features. We show that our models achieve competitive performance in natural image segmentation , with the Student-t mixtures reaching state-of-the art on boundaries scores .
After revision:  Second, we demonstrate how to use this flexible design can be extended to share class information between multiple mixture models. We apply our algorithm to artificial and natural image segmentation , with the Student-t mixtures reaching state-of-the art on boundaries scores .