  Accurate semantic labeling of image pixels is difficult because intra-class
variability is often greater than inter-class variability. In turn, fast
semantic segmentation is hard because accurate models are usually too
complicated to also run quickly at test-time. Our experience with building and
running semantic segmentation systems has also shown a reasonably obvious
bottleneck on model complexity, imposed by small training datasets. We
therefore propose two simple complementary strategies that leverage context to
give better semantic segmentation, while scaling up or down to train on
different-sized datasets.
  As easy modifications for existing semantic segmentation algorithms, we
introduce Decorrelated Semantic Texton Forests, and the Context Sensitive Image
Level Prior. The proposed modifications are tested using a Semantic Texton
Forest (STF) system, and the modifications are validated on two standard
benchmark datasets, MSRC-21 and PascalVOC-2010. In Python based comparisons,
our system is insignificantly slower than STF at test-time, yet produces
superior semantic segmentations overall, with just push-button training.
