基于分层图割的半度量MRF的MAP估计
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
摘要：
本文研究了具有任意一元势和半度量两两势的离散两两随机场的最大后验估计。对于这个问题，我们提出了一个准确的层次移动决策策略，每个移动计算效率通过解决一个ST-MINCUT问题。与以前的移动决策方法，例如广泛使用的a-扩展算法，我们的方法获得了标准的线性规划（LP）放松的重要特殊情况下的度量标记的保证。与现有的LP松弛求解器不同，例如通道点算法或树重加权消息传递，我们的方法速度明显更快，因为它在设计中只使用了有效的st-MINCUT算法。使用合成和真实数据的实验，我们表明，我们的技术优于几种常用的算法。
英文摘要：
We consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semimetric pairwise potentials. For this problem, we propose an accurate hierarchical move making strategy where each move is computed efficiently by solving an st-MINCUT problem. Unlike previous move making approaches, e.g. the widely used a-expansion algorithm, our method obtains the guarantees of the standard linear programming (LP) relaxation for the important special case of metric labeling. Unlike the existing LP relaxation solvers, e.g. interior-point algorithms or tree-reweighted message passing, our method is significantly faster as it uses only the efficient st-MINCUT algorithm in its design. Using both synthetic and real data experiments, we show that our technique outperforms several commonly used algorithms.
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