Fuzzy Relaxation Labeling Reconsidered

|The problem of assigning labels to objects given some relational constraints is occuring quite often in the eld of pattern recognition. Among the diierent solutions proposed there is a particularly successful one: Relaxation labeling. Relaxation labeling has turned out to be a useful iterative scheme for parallel information integration forcing neighborhood constraint satisfaction. Consequently there has been done an abundance of work on relaxation labeling since the seminal paper of Rosenfeld et.al..1]. There the authors discussed various models for relaxation labeling: discrete , probabilistic, non-linear probabilistic and fuzzy. Since then many new relaxation labeling schemes have been proposed only a few of which are incorporating methodologies from fuzzy set theory. It is shown that this is partly due to the traditional, but questionable classiication of relaxation labeling schemes. A new classiication is introduced which clariies the role of fuzzy relaxation labeling. The new clas-siication scheme is complemented by the introduction of a whole range of new fuzzy relaxation operators.