Daniel Glasner (Math & CS, The Weizmann Institute of Science)
We present an unsupervised, shape-based method for joint clustering of multiple
image segmentations. Given two or more closely-related images, such as close
frames in a video sequence or images of the same scene taken under different
lighting conditions, our method generates a joint segmentation of the images.
We introduce a novel contour-based representation that allows us to cast the
shape-based joint clustering problem as a quadratic semi-assignment problem.
Our score function is additive. We use complex-valued affinities to assess the
quality of matching the edge elements at the exterior bounding contour of
clusters, while ignoring the contributions of elements that fall in the
interior of the clusters. We further combine this contour-based score with
region information and use a linear programming relaxation to solve for the
joint clusters. We evaluate our approach on the occlusion boundary data-set of
Stein et al.
This is joint work with Shiv N. Vitaladevuni and Ronen Basri.