Optimising Similarity and Diversity in Image Retrieval

Abstract In this paper we describe the approaches adopted to gener-ate the runs submitted to ImageCLEFPhoto 2009 with an aim to pro-mote document diversity in rankings. Four of our runs are text based approaches that employ textual statistics extracted from the captions of images, i.e. MMR [1] as a state of the art method for result diversification, two approaches that combine relevance information and clustering tech-niques, and an instantiation of Quantum Probability Ranking Principle. The fifth run exploits visual features of the provided images to re-rank the initial results by means of Factor Analysis. The results reveal that our methods based on only text captions consistently improve the per-formance of the respective baseline, while the approach that combines visual features with textual statistics shows lower levels of improvements.