NDLI: On the dynamics of relaxation labeling processes

Content Provider	IEEE Xplore Digital Library
Author	Pelillo, M.
Copyright Year	1994
Description	Author affiliation: Dipartimento di Inf., Bari Univ., Italy (Pelillo, M.)
Abstract	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.
Starting Page	1006
Ending Page	1011
File Size	590945
Page Count	6
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374320
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Standards development Computer vision Pattern recognition Biological neural networks Neural networks Biology computing Concurrent computing Relaxation methods Numerical analysis
Content Type	Text
Resource Type	Article

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