Improving an Adaptive Image Interpretation System by Leveraging
Lihong C. Li, Vadim Bulitko, Russell Greiner, Ilya Levner · 2008
Abstract Automated image interpretation is an important task innumerous applications ranging from security systems to natural resource inventorization based on remote-sensing.Recently, a second generation of adaptive machine-learned image interpretation system (ADORE) has shown expert-level performance in several challenging domains. Its extension, MR ADORE, aims at removing the last vestiges ofhuman intervention still present in the original design of ADORE. Both systems treat the image interpretation pro-cess as a sequential decision making process guided by a machine-learned heuristic value function. This paper em-ploys a new leveraging algorithm for regression (R ESLEV)to improve the learnability of the heuristics in MR ADORE. Experiments show that RESLEV improves the system's per-formance if the base learners are weak. Further analysis discovers the difference between regression and decision-making problems, and suggests an interesting research direction. Keywords: adaptive image interpretation system, leverag-ing for regression, boosting, sequential decision making. 1.