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Howarth, Peter and Rüger, Stefan
(2005).
DOI: https://doi.org/10.1007/978-3-540-31865-1_32
URL: http://www-gsi.dec.usc.es/ecir05/
Abstract
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.
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About
- Item ORO ID
- 29878
- Item Type
- Conference or Workshop Item
- 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 or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2005 Springer-Verlag
- Depositing User
- Stefan Rüger