Automated selection of vision operator libraries with evolutionary algorithms

Adaptive image interpretation systems can learn optimal image interpretation policies for a given domain without human intervention. The policies are learned over an extensive generic image processing operator library. One of the principal weaknesses of the method lies with the large size of such libraries which can make machine learning process intractable. In this paper we demonstrate how evolutionary algorithms can be used to reduce the size of operator library thereby speeding up learning of the policy while still keeping human experts out of the development loop. Experiments in a challenging domain of forestry image interpretation exhibited a 93.3% reduction in the execution time, while maintaining the image interpretation accuracy within 5.5% of optimal.