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Pixel Labeling by Supervised Probabilistic Relaxation

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3 Author(s)
J. A. Richards ; School of Electrical Engineering and the Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN 47907; School of Electrical Engineering, University of New South Wales, Kensington, Australia. ; D. A. Landgrebe ; P. H. Swain

A simple modification to existing probabilistic relaxation procedures is suggested which allows the information contained in initial labels to exert an influence on the direction of relaxation throughout the process. In this manner, the initial labels assume more importance than with conventional algorithms and are used in combination with the outcome of relaxation at each iteration to produce a cooperative estimate of the correct label for a particular object. Pixel labeling examples are presented which show the performance that can be obtained with the modified algorithm. The procedure is readily generalized to allow other data to influence the process.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-3 ,  Issue: 2 )