Boundary detection is a fundamental problem in computer vision. However, boundary 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 ambiguity 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 boundaries 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 boundary detectors.