Semantic texton forests (STFs) are a form of random decision forest that can act as efficient and powerful low-level features for computer vision. Each decision tree acts directly on image pixels, and therefore does not need the expensive computation of filter-bank responses or local descriptors. STFs are extremely fast to both train and test, especially when compared with k-means clustering and nearest-neighbor assignment of feature descriptors. The nodes in the trees provide (i) an implicit hierarchical clustering into semantic textons, and (ii) an explicit local classification estimate. Additionally, the bag of semantic textons combines a histogram of semantic textons over an image region with a region prior category distribution. This method of employing random decision forests in the field of computer vision advanced the state-of-the-art in segmentation accuracy, and furthermore, provided at least a five-fold increase in execution speed over other techniques.