We propose a method for semantic parsing of images with regular
structure. The structured objects are modeled in a densely connected
CRF. The paper describes how to embody specific spatial relations in a
representation called Spatial Pattern Templates (SPT), which allows us
to capture regularity constraints of alignment and equal spacing in
pairwise and ternary potentials.
Assuming the input image is pre-segmented to salient regions the SPT
describe which segments could interact in the structured graphical
model. The model parameters are learnt to describe the formal language
of semantic labelings. Given an input image, a consistent labeling
over its segments linked in the CRF is recognized as a word from this
language.
We demonstrate the approach on the problem of facade image parsing and
show that results comparable with state of the art methods are
achieved.