Towards autonomous bootstrapping for life-long learning categorization tasks

We present an exemplar-based learning approach for incremental and life-long learning of visual categories. The basic concept of the proposed learning method is to subdivide the learning process into two phases. In the first phase we utilize supervised learning to generate an appropriate category seed, while in the second phase this seed is used to autonomously bootstrap the visual representation. This second learning phase is especially useful for assistive systems like a mobile robot, because the visual knowledge can be enhanced even if no tutor is present. Although for this autonomous bootstrapping no category labels are provided, we argue that contextual information is beneficial for this process. Finally we investigate the effect of the proposed second learning phase with respect to the overall categorization performance.