  Spatially Coherent Random Forest (SCRF) extends Random Forest to create
spatially coherent labeling. Each split function in SCRF is evaluated based on
a traditional information gain measure that is regularized by a spatial
coherency term. This way, SCRF is encouraged to choose split functions that
cluster pixels both in appearance space and in image space. In particular, we
use SCRF to detect contours in images, where contours are taken to be the
boundaries between different regions. Each tree in the forest produces a
segmentation of the image plane and the boundaries of the segmentations of all
trees are aggregated to produce a final hierarchical contour map. We show that
this modification improves the performance of regular Random Forest by about
10% on the standard Berkeley Segmentation Datasets. We believe that SCRF can be
used in other settings as well.
