IAM@ImageCLEFphoto 2009: Experiments on Maximising Diversity using Image Features

This paper describes the diversity enabled retrieval system constructed at Southampton for the ImageCLEFphoto 2009 task. The retrieval system used Terrier as the underlying textual indexing and retrieval system, and combined it with a technique for re-ranking the results by maximising the visual dissimilarity of retrieved images. The results show that our visual re-ranking method does indeed work at increasing the diversity in the top results, however, at the same time it causes a slight drop in precision. The text-based approach designed for handling the 'part 1 topics' of the task is also shown to perform very well.

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