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Relaxation Labeling with Learning Automata

Thathachar, Mandayam AL and Sastry, PS (1986) Relaxation Labeling with Learning Automata. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (2). pp. 256-268.

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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.

Item Type: Journal Article
Publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: IEEE
Additional Information: Copyright 1986 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Consistency;constraint satisfaction;learning automata;probabilistic relaxation;relaxation labeling.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 22 Jan 2010 07:16
Last Modified: 19 Sep 2010 05:43
URI: http://eprints.iisc.ac.in/id/eprint/22693

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