On the dynamics of relaxation labeling processes
On the dynamics of relaxation labeling processes
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弛豫标记过程的动力学
DOI：
10.1109/icnn.1994.374320
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发表时间：
1994
期刊：
影响因子：
--
通讯作者：
M. Pelillo
中科院分区：
文献类型：
--
作者：
M. Pelillo
松弛标记过程是一种并行迭代过程，用于解决某些约束满足问题，长期以来一直是计算机视觉和模式识别中的标准技术。本文表明，尽管它的启发式性质，松弛标记确实是密切相关的一个完善的一致性理论。它表明，当一定的对称性条件得到满足，该算法具有一个李雅普诺夫函数原来是（负）一个著名的一致性措施。这几乎是紧接着鲍姆和伊贡在马尔可夫链理论的背景下发展起来的一个强有力的结果。此外，可以看出，当对称性限制放松时，模型的大部分基本性质都得到了保留。本文提供的分析有助于加强松弛标记和某些神经网络模型之间的关系，并允许指出一些有趣的差异。此外，它为松弛过程的一些新应用铺平了道路。<<ETX>>
Relaxation labeling processes are parallel iterative procedures heuristically developed to solve certain constraint satisfaction problems, which have long become a standard technique in computer vision and pattern recognition. This paper shows that, despite its heuristic nature, relaxation labeling is indeed intimately related with a well-established theory of consistency. It is shown that, when a certain symmetry condition is met, the algorithm possesses a Liapunov function which turns out to be (the negative of) a well-known consistency measure. This follows almost immediately from a powerful result of Baum and Eagon developed in the context of Markov chain theory. Moreover, it is seen that most of the essential properties of the model are retained when the symmetry restriction is relaxed. The analysis provided in this paper contributes to strengthen the recognized relationship between relaxation labeling and certain neural network models and permits to point out some interesting differences. Moreover, it paves the way for a number of novel applications of relaxation processes.<<ETX>>