Can a probabilistic image annotation system be improved using a co-occurrence approach?
This event took place on Wednesday 26 November 2008 at 11:30
Ainhoa Llorente Coto KMi, The Open University
The research challenge that we address in this work is to examine whether a traditional automated annotation system can be improved by using external 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 datasets: Corel 5k and ImageCLEF 2008. For the Corel dataset, we obtain statistically significant better results while our algorithm appears in the top quartile of all methods submitted in ImageCLEF 2008. Regarding future work, we intend to apply Semantic Web technologies.
This event took place on Wednesday 26 November 2008 at 11:30
The research challenge that we address in this work is to examine whether a traditional automated annotation system can be improved by using external 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 datasets: Corel 5k and ImageCLEF 2008. For the Corel dataset, we obtain statistically significant better results while our algorithm appears in the top quartile of all methods submitted in ImageCLEF 2008. Regarding future work, we intend to apply Semantic Web technologies.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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