Liu, Haiming; Song, Dawei; Rüger, Stefan; Hu, Rui and Uren, Victoria
(2008).
|
(Version of Record)
Due to publisher licensing restrictions, this file is not available for public download |
| Google Scholar: | Look up in Google Scholar |
|---|
Abstract
Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure's retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.
| Item Type: | Conference or Workshop Item |
|---|---|
| Copyright Holders: | 2008 Springer-Verlag |
| Keywords: | dissimilarity measure; feature space; content-based image retrieval |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM) Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications |
| Item ID: | 11962 |
| Depositing User: | Users 8580 not found. |
| Date Deposited: | 08 Oct 2008 13:21 |
| Last Modified: | 29 Nov 2016 17:54 |
| URI: | http://oro.open.ac.uk/id/eprint/11962 |
| Share this page: |     |
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)