I will present a framework for improving the image index obtained by automated image annotation. Within this framework, the technique of keyword combination is used for fast image re-indexing based on initial automated annotations. It aims to tackle the challenges of limited vocabulary size and low annotation accuracies resulting from differences between training and test collections. It is useful for situations when these two problems are not anticipated at the time of annotation. I will show that based on example images from the automatically annotated collection, it is often possible to find multiple keyword queries that can retrieve new image concepts which are not present in the training vocabulary, and improve retrieval results of those that are already present. This can be done at a very small computational cost and at an acceptable performance tradeoff, compared to traditional annotation models. I will report results on TRECVID 2005, Getty Image Archive, and Web image datasets, the last two of which were specifically constructed to support realistic retrieval scenarios.

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