松驰标记法是指对图中的每个目标进行标记指派，利用先验上下文信息进行迭代，寻求最大协调标记集的一种方法。此文推导了一种新的概率松驰法，分析了随机松驰法的迭代公式，利用马尔科夫随机场（MRF）与吉布斯（Gibbs）分布的等价性来计算局部特性概率，用最大熵（ME）原理对条件邻域概率进行估计。最后对概率松驰法和随机松驰法进行了比较。

Relaxation labeling refers to a class of algorithms for assigning a label to each object in a graph, by iterating a transformation until a fixed point is reached. A probabilistic relaxation method is analytically derived in this paper, and a stochastic relaxation algorithm is also carried out step by step. We employ the MRF-Gibbs equivalence to calculate the local characteristics of the MRF, and take the maximum entropy (ME) estimate as the conditional neighborhood probabilities. At the last section of the paper, the two distinct approaches are compared and contrasted.