具有平面子问题的紧MRF松弛
Tightening MRF Relaxations with Planar Subproblems
摘要：
我们描述了一种新的技术计算下界的最小能量配置的平面马尔可夫随机场（MRF）。我们的方法连续增加了大量的约束条件，并强制执行的原始问题状态空间的二进制投影的一致性。这些约束表示在一个双分解框架，使用次梯度技术进行优化的子问题。我们考虑的完整约束集强制原始图的循环一致性。在实践中，我们发现，该方法收敛速度快，大多数问题，除了一些子问题，并优于现有的方法，一些有趣的类的硬潜力。
英文摘要：
We describe a new technique for computing lower-bounds on the minimum energy configuration of a planar Markov Random Field (MRF). Our method successively adds large numbers of constraints and enforces consistency over binary projections of the original problem state space. These constraints are represented in terms of subproblems in a dual-decomposition framework that is optimized using subgradient techniques. The complete set of constraints we consider enforces cycle consistency over the original graph. In practice we find that the method converges quickly on most problems with the addition of a few subproblems and outperforms existing methods for some interesting classes of hard potentials.
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