2 research outputs found
A Multiscale Framework for Challenging Discrete Optimization
Current state-of-the-art discrete optimization methods struggle behind when
it comes to challenging contrast-enhancing discrete energies (i.e., favoring
different labels for neighboring variables). This work suggests a multiscale
approach for these challenging problems. Deriving an algebraic representation
allows us to coarsen any pair-wise energy using any interpolation in a
principled algebraic manner. Furthermore, we propose an energy-aware
interpolation operator that efficiently exposes the multiscale landscape of the
energy yielding an effective coarse-to-fine optimization scheme. Results on
challenging contrast-enhancing energies show significant improvement over
state-of-the-art methods.Comment: 5 pages, 1 figure, To appear in NIPS Workshop on Optimization for
Machine Learning (December 2012). Camera-ready version. Fixed typos,
acknowledgements adde