Trading precision for speed: localised similarity functions

Authors:
Peter Howarth;Stefan Rüger
Affiliations:
Department of Computing, Imperial College London, London, UK;Department of Computing, Imperial College London, London, UK
Venue:
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Year:
2005

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Abstract

We have generalised a class of similarity measures that are designed to address the problems associated with indexing high-dimensional feature space. The features are stored and indexed component wise. For each dimension we retrieve only those objects close the query point and then apply a local distance function to this subset. Thus we can dramatically reduce the amount of data looked at. We have evaluated these distance measures within a content-based image retrieval (CBIR) framework to determine the trade-off between the percentage of the data retrieved and the precision. Our results show that up to 90% of the data can be ignored whilst maintaining, and in some cases improving, retrieval performance.