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.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Semantic Web and Knowledge Services is...
Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.
List all Semantic Web and Knowledge Services Projects
Check out these Hot Semantic Web and Knowledge Services Technologies:
List all Semantic Web and Knowledge Services Technologies
