Relaxation labeling with learning automata

Thathachar, Mandayam A. L. ; Sastry, P. S. (1986) Relaxation labeling with learning automata IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8 (2). pp. 256-268. ISSN 0162-8828

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Relaxation labeling processes are a class of mechanisms that solve the problem of assigning labels to objects in a manner that is consistent with respect to some domain-specific constraints. We reformulate this using the model of a team of learning automata interacting with an environment or a high-level critic that gives noisy responses as to the consistency of a tentative labeling selected by the automata. This results in an iterative linear algorithm that is itself probabilistic. Using an explicit definition of consistency we give a complete analysis of this probabilistic relaxation process using weak convergence results for stochastic algorithms. Our model can accommodate a range of uncertainties in the compatibility functions. We prove a local convergence result and show that the point of convergence depends both on the initial labeling and the constraints. The algorithm is implementable in a highly parallel fashion.

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ID Code:51346
Deposited On:28 Jul 2011 11:58
Last Modified:28 Jul 2011 11:58

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