Llorente, Ainhoa; Overell, Simon; Liu, Haiming; Hu, Rui; Rae, Adam; Zhu, Jianhan; Song, Dawei and Rüger, Stefan
(2009).
|
Due to copyright restrictions, this file is not available for public download |
| DOI (Digital Object Identifier) Link: | http://dx.doi.org/doi:10.1007/978-3-642-04447-2_79 |
|---|---|
| Google Scholar: | Look up in Google Scholar |
Abstract
This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an image annotation system. We observe that probabilistic image analysis by itself is not enough to describe the rich semantics of an image. Our hypothesis is that more accurate annotations can be produced by introducing additional knowledge in the form of statistical co-occurrence of terms. This is provided by the context of images that otherwise independent keyword generation would miss. We applied our algorithm to the dataset provided by ImageCLEF 2008 for the Visual Concept Detection Task (VCDT). Our algorithm not only obtained better results but also it appeared in the top quartile of all methods submitted in ImageCLEF 2008.
| Item Type: | Conference Item |
|---|---|
| Copyright Holders: | 2009 Springer-verlag Berlin Heidelberg |
| ISBN: | 3-642-04446-8, 978-3-642-04446-5 |
| ISSN: | 0302-9743 |
| Keywords: | automated image annotation; statistical co-occurrence; image analysis; semantic similarity |
| Academic Unit/Department: | Knowledge Media Institute Mathematics, Computing and Technology > Computing |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Item ID: | 23514 |
| Depositing User: | Kay Dave |
| Date Deposited: | 10 Nov 2010 11:35 |
| Last Modified: | 24 Oct 2012 22:34 |
| URI: | http://oro.open.ac.uk/id/eprint/23514 |
Actions (login may be required)
| View Item | |
| Report issue / request change |