The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

This paper addresses the problem of detecting and segmenting
partially occluded objects of a known category. We
first define a part labelling which densely covers the object.
Our Layout Consistent Random Field (LayoutCRF) model
then imposes asymmetric local spatial constraints on these
labels to ensure the consistent layout of parts whilst allowing
for object deformation. Arbitrary occlusions of the object
are handled by avoiding the assumption that the whole
object is visible. The resulting system is both efficient to
train and to apply to novel images, due to a novel annealed
layout-consistent expansion move algorithm paired with a
randomised decision tree classifier. We apply our technique
to images of cars and faces and demonstrate state-of-the-art
detection and segmentation performance even in the presence
of partial occlusion.