图逼近的MRF优化
MRF Optimization by Graph Approximation
摘要：
基于图割的算法在计算机视觉的能量最小化方面取得了巨大的成功。这些算法通过移动决策方法提供了多标签能量函数的近似解。这种方法将当前的解决方案与生成低能耗解决方案的建议相融合。因此，提出适当的建议是采取行动办法取得成功的必要条件。然而，没有太多的研究工作已经做了生成的“好”的建议，特别是对非度量的能量函数。在本文中，我们提出了一个独立的应用程序和能源为基础的方法来产生“好”的建议。有了这些建议，我们提出了一个基于图切割的移动决策算法称为GA融合（融合与基于图近似的建议）。大量的实验支持我们的建议生成是有效的跨不同类别的能量函数。所提出的算法优于其他真实和合成问题。
英文摘要：
Graph cuts-based algorithms have achieved great success in energy minimization for many computer vision applications. These algorithms provide approximated solutions for multi-label energy functions via move-making approach. This approach fuses the current solution with a proposal to generate a lower-energy solution. Thus, generating the appropriate proposals is necessary for the success of the move-making approach. However, not much research efforts has been done on the generation of "good" proposals, especially for non-metric energy functions. In this paper, we propose an application-independent and energy-based approach to generate "good" proposals. With these proposals, we present a graph cuts-based move-making algorithm called GA-fusion (fusion with graph approximation-based proposals). Extensive experiments support that our proposal generation is effective across different classes of energy functions. The proposed algorithm outperforms others both on real and synthetic problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
