Automated image interpretation is an important task with numerous applications. Until recently, designing such systems required extensive subject matter and computer vision expertise resulting in poor cross-domain portability and expensive maintenance. Recently, a machine-learned system ADORE (Adaptive Object Recognition) was successfully applied in an aerial image interpretation domain. In this paper we evaluate an extended version of this system, applied for the first time to a natural image interpretation domain. Performance of MR ADORE system is compared to the Hierarchical Hidden Markov Random Field (HHRMF) algorithm for supervised image annotation. We show that a hybrid system, easily constructed by utilizing the HHMRF models as operators within MR ADORE, performs significantly better than either of the systems on their own. To the best of our knowledge this is the first successful case of learning both vision operators and an adaptive control policy guiding their application in a single system.