Identification of novel classes in object class recognition

For novel class identification we propose to rely on the natural hierarchy of object classes, using a new approach to detect incongruent events. Here detection is based on the discrepancy between the responses of two different classifiers trained at different levels of generality: novelty is detected when the general level classifier accepts, and the specific level classifier rejects. Thus our approach is arguably more robust than traditional approaches to novelty detection, and more amendable to effective information transfer between known and new classes.We present an algorithmic implementation of this approach, show experimental results of its performance, analyze the effect of the underlying hierarchy on the task and show the benefit of using discriminative information for the training of the specific level classifier.