Continuous Learning of Simple Visual Concepts using Incremental Kernel Density Estimation

In this paper we propose a method for continuous learning of simple visual concepts. The method continuously associates words describing observed scenes with automatically extracted visual features. Since in our setting every sample is labelled with multiple concept labels, and there are no negative examples, reconstructive representations of the incoming data are used. The associated features are modelled with kernel density probability distribution estimates, which are built incrementally. The proposed approach is applied to the learning of object properties and spatial relations.