Alphabet SOUP: A Framework for Approximate Energy Minimization

Many problems in computer vision can be modeled using conditional Markov random fields (CRF). Since finding the maximum a posteriori (MAP) solution in such models is NP-hard, much attention in recent years has been placed on finding good approximate solutions. In particular, graph-cut based algorithms, such as -expansion, are tremendously successful at solving problems with regular potentials. However, for arbitrary energy functions, message passing algorithms, such as max-product belief propagation, are still the only resort. In this paper we describe a general framework for finding approximate MAP solutions of arbitrary energy functions. Our algorithm (called Alphabet SOUP for Sequential Optimization for Unrestricted Potentials) performs a search over variable assignments by iteratively solving subproblems over a reduced state-space. We provide a theoretical guarantee on the quality of the solution when the inner loop of our algorithm is solved exactly. We show that...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Stephen Gould (Stanford University), Fernando Amat (Stanford University), Daphne Koller (Stanford University)
• Keywords: Approximate Energy Minimization, Computer Vision, CVPR 2009