Understanding Image Structure via Hierarchical Shape Parsing

Exploring image structure is a long-standing yet important
research subject in the computer vision community. In
this paper, we focus on understanding image structure inspired
by the “simple-to-complex” biological evidence. A
hierarchical shape parsing strategy is proposed to partition
and organize image components into a hierarchical structure
in the scale space. To improve the robustness and flexibility
of image representation, we further bundle the image
appearances into hierarchical parsing trees. Image
descriptions are subsequently constructed by performing a
structural pooling, facilitating efficient matching between
the parsing trees. We leverage the proposed hierarchical
shape parsing to study two exemplar applications including
edge scale refinement and unsupervised “objectness”
detection. We show competitive parsing performance comparing
to the state-of-the-arts in above scenarios with far
less proposals, which thus demonstrates the advantage of
the proposed parsing scheme.