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.
Full text available as:
|
Due to copyright restrictions, this file is not available for public download
-
Due to copyright restrictions, this file is not available for public download
|
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.
| 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: |
29 Jul 2014 21:33 |
| URI: |
http://oro.open.ac.uk/id/eprint/29878 |
| Share this page: |
    |
Actions (login may be required)
