Image parsing remains difficult due to the need to combine local and contextual information when labeling a scene. We approach this problem by using the epitome as a prior over label configurations. Several properties make it suited to this task. First, it allows a condensed patch-based representation. Second, efficient E-M based learning and inference algorithms can be used. Third, non-stationarity is easily incorporated. We consider three existing priors, and show how each can be extended using the epitome.
Publication: CVPR 2009 Proceedings | full text (PDF)