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 L 1 and L 2 norms.
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Howarth, P., Rüger, S. (2005). Fractional Distance Measures for Content-Based Image Retrieval. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_32
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DOI: https://doi.org/10.1007/978-3-540-31865-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25295-5
Online ISBN: 978-3-540-31865-1
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