Discriminative dictionary learning with spatial priors

While smoothness priors are ubiquitous in analysis of visual information, dictionary learning for image analysis has traditionally relied on local evidences only. We present a novel approach to discriminative dictionary learning with neighborhood constraints. This is achieved by embedding dictionaries in a Conditional Random Field (CRF) and imposing labeldependent smoothness constraints on the resulting sparse codes at adjacent sites. This way, a smoothness prior is used while learning the dictionaries and not just to make inference. This is in contrast with competing approaches that learn dictionaries without such a prior. Pixel-level classification results on the Graz02 bikes dataset demonstrate that dictionaries learned in our discriminative setting with neighborhood smoothness constraints can equal the state-of-the-art performance of bottom-up (i.e. superpixel-based) segmentation approaches. Furthermore, we isolate the benets of our learning formulation and CRF inference to show that our dictionaries are more discriminative than dictionaries learned without such constraints even without CRF inference. An additional benet of our smoothness constraints is more stable learning which is a known problem for discriminative dictionaries.