  Existing angle-based contour descriptors suffer from lossy representation for
non-starconvex shapes. By and large, this is the result of the shape being
registered with a single global inner center and a set of radii corresponding
to a polar coordinate parameterization. In this paper, we propose AdaContour,
an adaptive contour descriptor that uses multiple local representations to
desirably characterize complex shapes. After hierarchically encoding object
shapes in a training set and constructing a contour matrix of all subdivided
regions, we compute a robust low-rank robust subspace and approximate each
local contour by linearly combining the shared basis vectors to represent an
object. Experiments show that AdaContour is able to represent shapes more
accurately and robustly than other descriptors while retaining effectiveness.
We validate AdaContour by integrating it into off-the-shelf detectors to enable
instance segmentation which demonstrates faithful performance. The code is
available at https://github.com/tding1/AdaContour.
