Robust shape analysis using multistrategy learning

This paper describes how to integrate subsymbolic and symbolic processes in order to create high-performance shape analysis systems. The specific methodology introduced integrates morphological processing and machine learning techniques such as genetic algorithms (GAs) and empirical inductive generalization. The optimal operators (defined as variable morphological structuring elements) evolved by GAs are used to derive discriminant feature vectors, which are then used by empirical inductive learning to generate rule-based class description in disjunctive normal form. The rule-based descriptions are finally optimized by removing small disjuncts in order to enhance the robustness of the shape analysis system. Experimental results are presented to illustrate the feasibility of the methodology for discriminating among classes of arbitrarily shaped objects, for learning the concepts of convexity and concavity, and for building robust recognition methods