Using second order statistics to enhance automated image annotation
http://data.open.ac.uk/oro/23507
is a Article , Academic article

Outgoing links

Property Object
Date 2009
Is part of repository
Status Peer reviewed
URI
  • http://data.open.ac.uk/oro/document/11087
  • http://data.open.ac.uk/oro/document/17965
  • http://data.open.ac.uk/oro/document/24966
  • http://data.open.ac.uk/oro/document/6099
Abstract 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.
Authors authors
Type
Label Llorente, Ainhoa and Rüger, Stefan (2009). Using second order statistics to enhance automated image annotation. In: Advances in Information Retrieval.
Same as 978-3-642-00958-7_52
Title Using second order statistics to enhance automated image annotation
Creator
Dataset Open Research Online
At The 31st European Conference on Information Retrieval (ECIR 2009)