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Llorente, Ainhoa; Overell, Simon; Liu, Haiming; Hu, Rui; Rae, Adam; Zhu, Jianhan; Song, Dawei and Rüger, Stefan
(2009).
DOI: https://doi.org/10.1007/978-3-642-04447-2_79
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.
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About
- Item ORO ID
- 23514
- Item Type
- Conference or Workshop Item
- 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 or 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 - Research Group
- Centre for Research in Computing (CRC)
- Copyright Holders
- © 2009 Springer-verlag Berlin Heidelberg
- Depositing User
- Kay Dave