Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models
Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models
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扰动和 MAP 随机场：使用离散优化从能量模型中学习和采样
DOI：
10.1109/iccv.2011.6126242
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
A. Yuille
中科院分区：
文献类型：
--
作者：
G. Papandreou;A. Yuille
提出了一种从离散标签上的能量函数诱导随机场的新方法。它相当于向能量势局部注入噪声，然后求扰动能量函数的全局最小值。由此产生的Perturb-and-MAP随机场利用了现代离散能量最小化算法的力量，有效地将它们转化为高效的随机抽样算法，从而将其范围扩展到通常的确定性设置之外。在这种方式下，我们可以享受到一个健全的概率框架的好处，比如能够表示解的不确定性或从训练数据中学习模型参数，同时完全绕过昂贵的马尔可夫链蒙特卡罗过程，通常与离散标签吉布斯马尔可夫随机场（mrf）相关。我们研究了所提出的模型的一些有趣的理论性质，并与吉布斯磁流变函数的理论性质并置，并解决了摄动过程的原则设计问题。我们提出了图像分割和场景标记的实验结果，说明了新的定性方面和提出的模型在实际计算机视觉应用中的潜力。
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding the global minimum of the perturbed energy function. The resulting Perturb-and-MAP random fields harness the power of modern discrete energy minimization algorithms, effectively transforming them into efficient random sampling algorithms, thus extending their scope beyond the usual deterministic setting. In this fashion we can enjoy the benefits of a sound probabilistic framework, such as the ability to represent the solution uncertainty or learn model parameters from training data, while completely bypassing costly Markov-chain Monte-Carlo procedures typically associated with discrete label Gibbs Markov random fields (MRFs). We study some interesting theoretical properties of the proposed model in juxtaposition to those of Gibbs MRFs and address the issue of principled design of the perturbation process. We present experimental results in image segmentation and scene labeling that illustrate the new qualitative aspects and the potential of the proposed model for practical computer vision applications.