  Contour and skeleton are two complementary representations for shape
recognition. However combining them in a principal way is nontrivial, as they
are generally abstracted by different structures (closed string vs graph),
respectively. This paper aims at addressing the shape recognition problem by
combining contour and skeleton according to the correspondence between them.
The correspondence provides a straightforward way to associate skeletal
information with a shape contour. More specifically, we propose a new shape
descriptor. named Skeleton-associated Shape Context (SSC), which captures the
features of a contour fragment associated with skeletal information. Benefited
from the association, the proposed shape descriptor provides the complementary
geometric information from both contour and skeleton parts, including the
spatial distribution and the thickness change along the shape part. To form a
meaningful shape feature vector for an overall shape, the Bag of Features
framework is applied to the SSC descriptors extracted from it. Finally, the
shape feature vector is fed into a linear SVM classifier to recognize the
shape. The encouraging experimental results demonstrate that the proposed way
to combine contour and skeleton is effective for shape recognition, which
achieves the state-of-the-art performances on several standard shape
benchmarks.
