This report presents 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. We 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.
We demonstrate that this can be done at a very small computational cost and at an acceptable performance
tradeoff, compared to traditional annotation models. We present a simple, robust, and computationally efficient
approach for finding an appropriate set of keywords for a given target concept. We 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.