Continuous Relaxation and Local Maxima Selection: Conditions for Equivalence
Continuous Relaxation and Local Maxima Selection: Conditions for Equivalence
复制标题
连续松弛和局部最大值选择：等价条件
DOI：
10.1109/tpami.1981.4767069
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发表时间：
1979
影响因子：
23.6
通讯作者：
J. Mohammed
中科院分区：
文献类型：
--
作者：
S. Zucker;Y. Leclerc;J. Mohammed
松弛标记过程是用于使用上下文信息来减少局部歧义的一类迭代算法。本文介绍了一个新的角度来看放松，认为它是一个过程，重新排序标签附加到节点的图。这个新的角度是用来建立松弛和另一个广泛使用的算法，局部最大值选择之间的正式等价。的等价性指定的条件下，一个家庭的合作松弛算法，它概括了众所周知的，分解成纯粹的本地。由于这些条件也足以保证松弛过程的收敛性，它们可以作为停止准则。我们认为，这些等价物是必要的适当的应用程序的放松和最大值选择在复杂的语音和视觉理解系统。
Relaxation labeling processes are a class of iterative algorithms for using contextual information to reduce local ambiguities. This paper introduces a new perspective toward relaxation-that of considering it as a process for reordering labels attached to nodes in a graph. This new perspective is used to establish the formal equivalence between relaxation and another widely used algorithm, local maxima selection. The equivalence specifies conditions under which a family of cooperative relaxation algorithms, which generalize the well-known ones, decompose into purely local ones. Since these conditions are also sufficient for guaranteeing the convergence of relaxation processes, they serve as stopping criteria. We feel that equivalences such as these are necessary for the proper application of relaxation and maxima selection in complex speech and vision understanding systems.