A framework for cooperative segmentation based on the multi-agents paradigm.

In the image interpretation domain, one of very scrupulous stages rests in the partitioning of pixels into principal components that correspond to the part of the image. However, the approaches used for a segmentation process, either have a very strong specificity or require many sensible parameters, which make their use in a general case very difficult. Using them in inappropriate cases leads to very bad performance in terms of result quality and relevance. Broadly speaking, to obtain an acceptable segmentation result, we have to choose the appropriate method which best adapted method and we have to turn it very carefully. However, even thought the need for an adaptable methodology has been long recognized, few proposes have been appeared. In this paper we present a cooperative and concurrent framework for image segmentation. Our approach integrate several region based methods and detector based boundary finding using Multi-Agents System (MAS). Thanks to natural MAS properties, the image analysis is done at several semantic levels. The novelty of our approach is that is a multi directional cooperation whereby each separate method improves its own result as well as the global one through mutual cooperation and information sharing.