We present a probabilistic method for segmenting instances of a particular object category within an image. Our approach overcomes the deficiencies of previous segmentation techniques based on traditional grid conditional random fields (CRF), namely that (i) they require the user to provide seed pixels for the foreground and the background; and (ii) they provide a poor prior for specific shapes due to the small neighborhood size of grid CRF. Specifically, we automatically obtain the pose of the object in a given image instead of relying on manual interaction. Furthermore, we employ a probabilistic model which includes shape potentials for the object to incorporate top-down information that is global across the image, in addition to the grid clique potentials which provide the bottom-up information used in previous approaches. The shape potentials are provided by the pose of the object obtained using an object category model. We represent articulated object categories using a novel layered pictorial structures model. Non-articulated object categories are modelled using a set of exemplars. These object category models have the advantage that they can handle large intra-class shape, appearance and spatial variation. We develop an efficient method, OBJCUT, to obtain segmentations using our probabilistic framework. Novel aspects of this method include: (i) efficient algorithms for sampling the object category models of our choice; and (ii) the observation that a sampling-based approximation of the expected log likelihood of the model can be increased by a single graph cut. Results are presented on several articulated (e.g. animals) and non-articulated (e.g. fruits) object categories. We provide a favorable comparison of our method with the state of the art in object category specific image segmentation, specifically the methods of Leibe & Schiele and Schoenemann & Cremers.