Shape analysis using hybrid learning

This paper is concerned with hybrid learning and it describes how to combine evolution and symbolic learning for shape analysis. The methodology introduced in this paper integrates genetic algorithms (GAs) characteristic of evolutionary learning with empirical inductive generalization characteristic of symbolic learning. GAs evolve operators that discriminate among image classes comprising different shapes, where the operators are defined as variable morphological structuring elements that can be sequenced as program forms. The optimal operators 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 (DNF). The GA constitutes the data-driven, performance-oriented part of the shape analysis system, while the empirical inductive generalization is the model-driven part of the system. 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 our novel methodoloy for discriminating among classes of different shaped objects and for learning the concepts of convexity and concavity. Copyright (C) 1996 Pattern Recognition Society. Published by Elsevier Science Ltd.