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关于多标签MRF的部分最优性
On Partial Optimality in Multi-label MRFs |
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| 课程网址: |
http://videolectures.net/icml08_shekhovtsov_opo/
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| 主讲教师: |
Alexander Shekhovtsov
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| 开课单位: |
格拉茨科技大学
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| 开课时间: |
2008-08-29
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| 课程语种: |
英语
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| 中文简介: |
我们考虑优化多标签MRF的问题，这通常是NP难以在低级计算机视觉中无处不在。解决方案的一种方法是将其表示为整数规划问题并放宽完整性约束。我们在本文中考虑的方法是首先将多标签MRF转换为等效的二进制标签MRF然后放松它。我们的主要贡献是对这种新放松的理论研究。我们还展示了这种方法如何与最近开发的基于屋顶二元性的优化技术结合使用，该优化技术具有可以找到二元MRF的部分（或有时是完整的）最优解的期望特性。此属性使我们能够本地化（限制）多标签MRF的任何随机变量的最佳标签所在的标签范围。在许多情况下，这些定位导致多标记MRF的部分最优解。此外，运行标准的MRF求解器，例如， TRW S，在这种限制能量上比在原始无限制能量上运行要快得多。我们演示了如何使用我们的方法来挑战计算机视觉问题。我们的实验结果表明，从我们的研究中得出的方法优于竞争方法，以最大限度地减少多标记MRF。
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| 课程简介: |
We consider the problem of optimizing multi-label MRFs, which is in general NP-hard and ubiquitous in low-level computer vision. One approach for its solution is to formulate it as an integer programming problem and relax the integrality constraints. The approach we consider in this paper is to first convert the multi-label MRF into an equivalent binary-label MRF and then to relax it. Our key contribution is a theoretical study of this new relaxation. We also show how this approach can be used in combination with recently developed optimization techniques based on roof-duality which have the desired property that a partial (or sometimes the complete) optimal solution of the binary MRF can be found. This property enables us to localize (restrict) the range of labels where the optimal label for any random variable of the multi-label MRF lies. In many cases these localizations lead to a partially optimal solution of the multi-label MRF. Further, running standard MRF solvers, e.g. TRW-S, on this restricted energy is much faster than running them on the original unrestricted energy. We demonstrate the use of our methods on challenging computer vision problems. Our experimental results show that methods derived from our study outperform competing methods for minimizing multi-label MRFs.
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| 关 键 词: |
多标签; 计算机视觉; 二进制标签
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-21：lxf
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| 阅读次数: |
91
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