Generative learning of visual concepts using multiobjective genetic programming

This paper introduces a novel method of visual learning based on Genetic Programming, which evolves a population of individuals (image analysis programs) that process attributed visual primitives derived from raw raster images. The goal is to evolve an image analysis program that correctly recognizes the training concept (shape). The approach uses generative evaluation scheme: individuals are rewarded for reproducing the shape of the object being recognized using graphical primitives and elementary background knowledge encoded in prede ned operators. Evolutionary run is driven by a multiobjective tness function to prevent premature convergence and enable e ective exploration of the space of solutions. We present the method in detail and verify it experimentally on the task of learning two visual concepts from examples.