CMPUT 651 Final Report HSMM : Heuristic Search with Meta - Models for Vision

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 the machine learning process intractable. We demonstrate how evolutionary algorithms can be used to reduce the size of the 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 95% reduction in the execution time, while maintaining the image interpretation accuracy of the full library. Adding seeding and elitism to our novel method serves to further improve the resulting operator sets.