Decomposition and decentralization techniques in relaxation labeling

Abstract The problem of assigning names to objects in a scene is central in image analysis. Relaxation labeling techniques provide ways of taking into account contextual information and evolve from decisions based upon local measurements only to decisions based also on a given “world model”. When the number of objects and the number of possible names grows larger as it does in practice these techniques become more and more difficult to use (the curse of dimensionality). In this paper we propose two classes of algorithms that reduce this difficulty. The main idea is to replace a “global” problem of large dimensionality with a series “local” problems of smaller dimensionality while insuring that the solutioos of the “local” problems are the same as those of the “global” ones. The notion of coordinability is central to this approach. The behavior of these algorithms is presented in a simple example.