A Subspace Approach to Layer Extraction

Representing images with layers has many important applications,
such as video compression, motion analysis, and
3D scene analysis. This paper presents an approach to reliably
extracting layers from images by taking advantages
of the fact that homographies induced by planar patches in
the scene form a low dimensional linear subspace. Layers
in the input images will be mapped in the subspace, where
it is proven that they form well-defined clusters and can be
reliably identified by a simple mean-shift based clustering
algorithm. Global optimality is achieved since all valid regions
are simultaneously taken into account, and noise can
be effectively reduced by enforcing the subspace constraint.
Good layer descriptions are shown to be extracted in the
experimental results.