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Using second order statistics to enhance automated image annotation

Llorente, Ainhoa and Rüger, Stefan (2009). Using second order statistics to enhance automated image annotation. In: Advances in Information Retrieval.

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We examine whether a traditional automated annotation system can be improved by using background knowledge. Traditional means any machine learning approach together with image analysis techniques. We use as a baseline for our experiments the work done by Yavlinsky et al. who deployed non-parametric density estimation. 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 test our algorithm with two different datasets: Corel 5k and ImageCLEF 2008. For the Corel 5k dataset, we obtain significantly better results while our algorithm appears in the top quartile of all methods submitted in ImageCLEF 2008.

Item Type: Conference or Workshop Item
Copyright Holders: 2009 Springer-Verlag
Keywords: automated image annotation; statistical analysis; word co-occurrence; 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)
Item ID: 23507
Depositing User: Kay Dave
Date Deposited: 12 Oct 2010 11:07
Last Modified: 09 Dec 2018 04:47
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