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马尔可夫随机场中的多样化M-Best的解法
Diverse M-Best Solutions in Markov Random Fields |
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| 课程网址: |
http://videolectures.net/eccv2012_batra_markov/
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| 主讲教师: |
Laurent Itti, Dhruv Batra, Ramin Zabih
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| 开课单位: |
佐治亚理工学院
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| 开课时间: |
2012-11-12
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| 课程语种: |
英语
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| 中文简介: |
已经针对用于在概率（随机场）模型中获得最高概率（MAP）配置的算法进行了很多努力。在许多情况下，人们可以从额外的高概率解决方案中受益。用于计算M个最可能配置的当前方法产生倾向于非常类似于MAP解决方案并且彼此非常相似的解决方案。这通常是不合需要的属性。在本文中，我们提出了一种多样化MBest问题的算法，该算法涉及在离散概率模型下找到一组多样的高概率解。给定测量两个解的紧密度的相异度函数，我们的公式涉及最大化概率和不相似度与先前解的线性组合。我们的公式推广了MBest MAP问题，我们表明，对于某些不相似函数族，我们可以保证这些解决方案可以像MAP解决方案一样容易找到。
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| 课程简介: |
Much effort has been directed at algorithms for obtaining the highest probability (MAP) configuration in probabilistic (random field) models. In many situations, one could benefit from additional high probability solutions. Current methods for computing the M most probable configurations produce solutions that tend to be very similar to the MAP solution and each other. This is often an undesirable property. In this paper we propose an algorithm for the Diverse MBest problem, which involves finding a diverse set of highly probable solutions under a discrete probabilistic model. Given a dissimilarity function measuring closeness of two solutions, our formulation involves maximizing a linear combination of the probability and dissimilarity to previous solutions. Our formulation generalizes the MBest MAP problem and we show that for certain families of dissimilarity functions we can guarantee that these solutions can be found as easily as the MAP solution.
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| 关 键 词: |
概率; 离散概率; 相异度函数
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-03-20：lxf
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| 阅读次数: |
46
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