OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues

—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 1) they require the user to provide seed pixels for the foreground and the background and 2) 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 layer...

• Type: Journal
• Year: 2010
• Where: PAMI
• Authors: M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
• Keywords: Object Category, Object Category Models, PAMI 2010, Shape Potentials