Many state-of-the-art segmentation algorithms rely on Markov and Conditional Random Fields designed to enforce spatial and global consistency constraints. This often results in fairly complex designs. As a result, estimating the parameters or computing the best Maximum-A-Posteriori (MAP) assignment for such models become a computationnaly expensive task. In this paper, we argue that similar levels of performance can be achieved on the PASCAL and MSRC datasets using a much simpler design that essentially ignores those constraints. It replaces them by global features that leverage evidence from the whole image and uses them to bias the preference of individual pixels. This does not prove that spatial and consistency constraints are not important but points to the conclusion that they should be validated in a larger context.