@inproceedings{5956,
  title = {Object Localization with Global and Local Context Kernels},
  journal = {Proceedings of the British Machine Vision Conference 2009 (BMVC 2009)},
  booktitle = {British Machine Vision Conference 2009},
  abstract = {Recent research has shown that the use of contextual cues significantly improves performance
  in sliding window type localization systems. In this work, we propose a method
  that incorporates both global and local context information through appropriately defined
  kernel functions. In particular, we make use of a weighted combination of kernels defined
  over local spatial regions, as well as a global context kernel. The relative importance of
  the context contributions is learned automatically, and the resulting discriminant function
  is of a form such that localization at test time can be solved efficiently using a branch
  and bound optimization scheme. By specifying context directly with a kernel learning
  approach, we achieve high localization accuracy with a simple and efficient representation.
  This is in contrast to other systems that incorporate context for which expensive
  inference needs to be done at test time. We show experimentally on the PASCAL VOC
  datasets that the inclusion of context can significantly improve localization performance,
  provided the relative contributions of context cues are learned appropriately.},
  pages = {1-11},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2009},
  author = {Blaschko, M. and Lampert, CH.},
  month_numeric = {9}
}