Boundary detection is a fundamental problem in computer vision. However, bound-ary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the am-biguity of the local signal. In this paper we investigate incorporating a priori information into boundary detection. We learn a probabilistic model that describes a prior for object boundaries over small patches of the image. We then incorporate this boundary model into a mixture of multiscale conditional random fields, where the mixture components represent different contexts formed by clustering overall spatial distributions of bound-aries across images and image regions (vistas). We demonstrate this approach using challenging real-world road scenes. Importantly, we show that recent spectral methods that have been used in state-of-the-art boundary detection algorithms do not generalize well to these complex scenes. We show that our algorithm successfully learns these boundary distributions and can exploit this knowledge to improve state-of-the-art bound-ary detectors. 1