A Robust PCA algorithm for building representations from panoramic images

We present an artifficial cognitive system for learning visual concepts. It comprises of vision, communication and manipulation sub- systems, which provide visual input, enable verbal and non-verbal communication with a tutor and allow interaction with a given scene. The main goal is to learn associations between automatically extracted visual features and words that describe the scene in an open-ended, continuous manner. In particular, we address the problem of cross-modal learning of visual properties and spatial relations. We introduce and analyse several learning modes requiring diffeerent levels of tutor supervision.