@inproceedings{6336,
  title = {Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval},
  journal = {Proceedings of the Twelfth IEEE International Conference on Computer Vision (ICCV 2009)},
  booktitle = {ICCV 2009},
  abstract = {We study the task of detecting the occurrence of objects
  in large image collections or in videos, a problem that combines
  aspects of content based image retrieval and object
  localization. While most previous approaches are either
  limited to special kinds of queries, or do not scale to large
  image sets, we propose a new method, efficient subimage
  retrieval (ESR), which is at the same time very flexible and
  very efficient. Relying on a two-layered branch-and-bound
  setup, ESR performs object-based image retrieval in sets of
  100,000 or more images within seconds. An extensive evaluation
  on several datasets shows that ESR is not only very
  fast, but it also achieves detection accuracies that are on
  par with or superior to previously published methods for
  object-based image retrieval.},
  pages = {987-994},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = oct,
  year = {2009},
  author = {Lampert, CH.},
  doi = {10.1109/ICCV.2009.5459359},
  month_numeric = {10}
}