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Fractional distance measures for content-based image retrieval

Howarth, Peter and Rüger, Stefan (2005). Fractional distance measures for content-based image retrieval. In: 27th European Conference on Information Retrieval (ECIR '05), 21-23 March 2005, Santiago de Compostela, Spain, Springer LNCS 3408, pp. 447–456.

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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.

Item Type: Conference Item
Copyright Holders: 2005 Springer-Verlag
ISSN: 0302-9743
Extra Information: Published in: D.E. Losada and J.M. Fernandez-Luna (Eds.): ECIR 2005, LNCS 3408, pp. 447–456, 2005
Academic Unit/Department: Knowledge Media Institute
Item ID: 29878
Depositing User: Stefan Rüger
Date Deposited: 27 Oct 2011 14:28
Last Modified: 06 Oct 2015 23:02
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