  Scene understanding remains a significant challenge in the computer vision
community. The visual psychophysics literature has demonstrated the importance
of interdependence among parts of the scene. Yet, the majority of methods in
computer vision remain local. Pictorial structures have arisen as a fundamental
parts-based model for some vision problems, such as articulated object
detection. However, the form of classical pictorial structures limits their
applicability for global problems, such as semantic pixel labeling. In this
paper, we propose an extension of the pictorial structures approach, called
pixel-support parts-sparse pictorial structures, or PS3, to overcome this
limitation. Our model extends the classical form in two ways: first, it defines
parts directly based on pixel-support rather than in a parametric form, and
second, it specifies a space of plausible parts-based scene models and permits
one to be used for inference on any given image. PS3 makes strides toward
unifying object-level and pixel-level modeling of scene elements. In this
report, we implement the first half of our model and rely upon external
knowledge to provide an initial graph structure for a given image. Our
experimental results on benchmark datasets demonstrate the capability of this
new parts-based view of scene modeling.
