Active online confidence boosting for efficient object classification

We present a novel efficient algorithm for object classification. Our method is based on the active learning framework, in which training and classification are performed in loops, and new ground truth labels are queried from the supervisor in each loop. Our underlying classifier is from the family of boosting methods, but in contrast to earlier methods, our Confidence Boosting particularly focusses on misclassified samples that have a high classification confidence associated. We show that weighting these samples more than others leads to a decrease of overconfidence, for which we give a formal definition. As a result, our classifier is better suited for active learning, leading to steeper learning curves and less required label queries. We show the benefits of our approach on standard data sets from machine learning and robotics.