Publications
Authors:
  • Thomas Mensink , Verbeek Jakob , Gabriela Csurka
Citation:
BMVC 2010 - the 21st British Machine Vision Conference - Aberystwyth , United Kingdom - 31st August-3rd September, 2010
Full paper available on <a href=http://www.bmva.org/bmvc/2010/conference/paper20/paper20.pdf > BMVC Website </a>
Abstract:
Automatic image annotation is an important tool for keyword-based image retrieval, providing a textual index for non-annotated images. Many image auto annotation methods are based on visual similarity between images to be annotated and images in a training corpus. The annotations of the most similar training images are transferred to the image to be annotated. In this paper we consider using also similarities among the training images, both visual and textual, to derive pseudo relevance models, as well as crossmedia relevance models. We extend a recent state-of-the-art image annotation model to
incorporate this information. On two widely used datasets (COREL and IAPR) we show experimentally that the pseudo-relevance models improve the annotation accuracy.
Year:
2010
Report number:
2010/010