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.

本文旨在解决汽车语义部分分割问题（即将汽车内的每个像素分配到其中一个部分（例如车身，车窗，灯光，车牌和车轮））。我们将其作为一个标志性识别问题，并提出一种新颖的图形模型混合方法，该方法动态地将标志性点与一系列的段层次耦合起来。通过这种耦合，我们的模型能够利用局部图像内容以及空间变形的方面，保证相同部分内的段的外观一致性。通过动态编程实现汽车部分分割，包括找到标志点与层次中段之间的最佳耦合。我们在 PASCAL VOC 2010 汽车图像子集和三维物体类别数据集（CAR3D）的汽车子集上评估我们的方法。我们证明了使用分割外观一致性在部分定位和分割的精度方面的有效性。