@inproceedings{5070,
  title = {Beyond Sliding Windows: Object Localization by Efficient Subwindow Search},
  journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008)},
  booktitle = {CVPR 2008},
  abstract = {Most successful object recognition systems rely on binary
  classification, deciding only if an object is present or
  not, but not providing information on the actual object location.
  To perform localization, one can take a sliding window
  approach, but this strongly increases the computational
  cost, because the classifier function has to be evaluated over
  a large set of candidate subwindows.
  In this paper, we propose a simple yet powerful branchand-
  bound scheme that allows efficient maximization of a
  large class of classifier functions over all possible subimages.
  It converges to a globally optimal solution typically
  in sublinear time. We show how our method is applicable
  to different object detection and retrieval scenarios. The
  achieved speedup allows the use of classifiers for localization
  that formerly were considered too slow for this task,
  such as SVMs with a spatial pyramid kernel or nearest
  neighbor classifiers based on the 2-distance. We demonstrate
  state-of-the-art performance of the resulting systems
  on the UIUC Cars dataset, the PASCAL VOC 2006 dataset
  and in the PASCAL VOC 2007 competition.},
  pages = {1-8},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = jun,
  year = {2008},
  note = {Best paper award},
  slug = {5070},
  author = {Lampert, CH. and Blaschko, MB. and Hofmann, T.},
  month_numeric = {6}
}