Online learning with novelty detection in human-guided road tracking

Current image processing and pattern recognition algorithms are not robust enough to make automated remote sensing image interpretation feasible. For this reason, we need to develop image interpretation systems that rely on human guidance. In this paper, we tackle the problem of semiautomatic road tracking in aerial photos. We propose an online learning approach that naturally integrates inputs from human experts with computational algorithms to learn road tracking. Human inputs provide the online learner with training examples to generate road predictors. An ensemble of road predictors is learned incrementally and used to automatically track roads. When novel situations are encountered, control is returned back to the human expert to initialize a new training and tracking iteration. Our approach is computationally efficient, and it can rapidly adapt to dynamic situations where the image feature distributions change. Experimental results confirm that our approach is effective and superior to existing methods.