Optimization-based image analysis dealing with symbolic constraints using hierarchical multi-agent system

The paper describes a method for understanding an image where desired objects have part-of relationships between them. This method is based on a hierarchical multi-agent system, where each agent takes charge of a desired object and tries to extract it using knowledge on its features. Since users can define this knowledge freely without any modification of the algorithm, this method is applicable to various problems of image analysis by changing the knowledge. Moreover, the agents in this system use symbolic constraints and evaluation measurements on the desired objects. They are defined in the knowledge each agent has and used to obtain the desired results where obtained objects are evaluated highly in terms of the evaluation measurements and satisfy their plausible relationships defined symbolically. To verify our method experimentally, we applied it to problems of line drawing recognition and character segmentation