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'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.
AUTOMATED IMAGE ANNOTATION BASED UPON META-LEARNING OVER TIME