Llorente, Ainhoa; Motta, Enrico and Rüger, Stefan
(2009).
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(Version of Record)
Due to publisher licensing restrictions, this file is not available for public download |
| DOI (Digital Object Identifier) Link: | https://doi.org/10.1007/978-3-642-10543-2_22 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
This paper describes a novel approach that automatically refines the image annotations generated by a non-parametric density estimation model. We re-rank these initial annotations following a heuristic algorithm, which uses semantic relatedness measures based on keyword correlation on the Web. Existing approaches that rely on keyword co-occurrence can exhibit limitations, as their performance depend on the quality and coverage provided by the training data. Additionally, WordNet based correlation approaches are not able to cope with words that are not in the thesaurus. We illustrate the effectiveness of our Web-based approach by showing some promising results obtained on two datasets, Corel 5k, and ImageCLEF2009.
| Item Type: | Conference or Workshop Item |
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| Copyright Holders: | 2009 Springer-Verlag Berlin Heidelberg |
| ISSN: | 0302-9743 |
| Keywords: | automated image annotation; normalized Google distance; semantic similarity |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Research Group: | Centre for Research in Computing (CRC) |
| Item ID: | 23440 |
| Depositing User: | Kay Dave |
| Date Deposited: | 14 Oct 2010 11:33 |
| Last Modified: | 02 May 2018 13:16 |
| URI: | http://oro.open.ac.uk/id/eprint/23440 |
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