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Exploiting term co-occurrence for enhancing automated image annotation

Llorente, Ainhoa; Overell, Simon; Liu, Haiming; Hu, Rui; Rae, Adam; Zhu, Jianhan; Song, Dawei and Rüger, Stefan (2009). Exploiting term co-occurrence for enhancing automated image annotation. In: Evaluating Systems for Multilingual and Multimodal Information Access, 9th Workshop of the Cross-Language Evaluation Forum, 17-19 Sep 2008, Aarhus, Denmark, Springer, pp. 632–639.

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DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1007/978-3-642-04447-2_79
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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 & Communications
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
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