A Framework for Continuous Learning of Simple Visual Concepts
Danijel Skočaj, Barry Ridge, Gregor Berginc, Aleš Leonardis · 2007
Continuous learning, incremental learning, incremental kernel density estimation, learning associations. 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. 1