  This paper addresses the problem of semantic part parsing (segmentation) of
cars, i.e.assigning every pixel within the car to one of the parts (e.g.body,
window, lights, license plates and wheels). We formulate this as a landmark
identification problem, where a set of landmarks specifies the boundaries of
the parts. A novel mixture of graphical models is proposed, which dynamically
couples the landmarks to a hierarchy of segments. When modeling pairwise
relation between landmarks, this coupling enables our model to exploit the
local image contents in addition to spatial deformation, an aspect that most
existing graphical models ignore. In particular, our model enforces appearance
consistency between segments within the same part. Parsing the car, including
finding the optimal coupling between landmarks and segments in the hierarchy,
is performed by dynamic programming. We evaluate our method on a subset of
PASCAL VOC 2010 car images and on the car subset of 3D Object Category dataset
(CAR3D). We show good results and, in particular, quantify the effectiveness of
using the segment appearance consistency in terms of accuracy of part
localization and segmentation.
