Efficient optimization for Hierarchically-structured Interacting Segments
(HINTS)
Abstract
We propose an effective optimization algorithm for a
general hierarchical segmentation model with geometric interactions between segments. Any given tree can specify
a partial order over object labels defining a hierarchy. It
is well-established that segment interactions, such as inclusion/exclusion and margin constraints, make the model
significantly more discriminant. However, existing optimization methods do not allow full use of such models.
Generic a-expansion results in weak local minima, while
common binary multi-layered formulations lead to nonsubmodularity, complex high-order potentials, or polar domain unwrapping and shape biases. In practice, applying
these methods to arbitrary trees does not work except for
simple cases. Our main contribution is an optimization
method for the Hierarchically-structured Interacting Segments (HINTS) model with arbitrary trees. Our Path-Moves
algorithm is based on multi-label MRF formulation and can
be seen as a combination of well-known a-expansion and
Ishikawa techniques. We show state-of-the-art biomedical
segmentation for many diverse examples of complex trees.