Generalized Darting Monte Carlo
Generalized Darting Monte Carlo
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DOI：
10.1016/j.patcog.2011.02.006
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
C. Sminchisescu;M. Welling
中科院分区：
文献类型：
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作者：
C. Sminchisescu;M. Welling
马尔可夫链蒙特卡罗采样器的主要缺点之一是它们不能在目标分布的模式之间混合。在本文中，我们表明，提前了解这些模式的位置可以纳入MCMC采样器通过引入跳模移动，满足详细的平衡。所提出的采样算法通过局部MCMC移动（例如扩散或混合蒙特卡罗）探索局部模式结构，但此外还使用一组全局移动正确地表示不同模式的相对强度。这种“跳模”MCMC采样器可以被看作是飞镖方法[1]的推广。我们说明了一个“真实的世界”的视觉应用推断3-D人体姿势从单一的2-D图像的方法。
One of the main shortcomings of Markov chain Monte Carlo samplers is their inability to mix between modes of the target distribution. In this paper we show that advance knowledge of the location of these modes can be incorporated into the MCMC sampler by introducing mode-hopping moves that satisfy detailed balance. The proposed sampling algorithm explores local mode structure through local MCMC moves (eg diffusion or Hybrid Monte Carlo) but in addition also represents the relative strengths of the different modes correctly using a set of global moves. This ‘mode-hopping’MCMC sampler can be viewed as a generalization of the darting method [1]. We illustrate the method on a ‘real world’vision application of inferring 3-D human body pose from single 2-D images.