Fractional Distance Measures for Content-Based Image Retrieval


We have applied the concept of fractional distance measures, proposed by Aggarwal et al. [1], to content-based image retrieval. Our experiments show that retrieval performances of these measures consistently outperform the more usual Manhattan and Euclidean distance metrics when used with a wide range of high-dimensional visual features. We used the parameters learnt from a Corel dataset on a variety of different collections, including the TRECVID 2003 and ImageCLEF 2004 datasets. We found that the specific optimum parameters varied but the general performance increase was consistent across all 3 collections. To squeeze the last bit of performance out of a system it would be necessary to train a distance measure for a specific collection. However, a fractional distance measure with parameter p = 0.5 will consistently outperform both L1 and L2 norms.

DOI: 10.1007/978-3-540-31865-1_32

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@inproceedings{Howarth2005FractionalDM, title={Fractional Distance Measures for Content-Based Image Retrieval}, author={Peter Howarth and Stefan M. R{\"{u}ger}, booktitle={ECIR}, year={2005} }