a principled , probabilistic approach to meta - learning acts as a go - between for a ‘ black - box ’ image annotation system and its users . inspired by inductive transfer , the approach harnesses available information , including the black - box model &# 39 ; s performance , the image representations , and a semantic lexicon ontology . being computationally ‘ lightweight .’ the meta - learner efficiently re - trains over time , to improve and / or adapt to changes . the black - box annotation model is not required to be re - trained , allowing computationally intensive algorithms to be used . both batch and online annotation settings are accommodated . a “ tagging over time ” approach produces progressively better annotation , significantly outperforming the black - box as well as the static form of the meta - learner , on real - world data .