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Beyond Sliding Windows: Object Localization by Efficient Subwindow Search

2008

Conference Paper

ei


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.

Author(s): Lampert, CH. and Blaschko, MB. and Hofmann, T.
Book Title: CVPR 2008
Journal: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008)
Pages: 1-8
Year: 2008
Month: June
Day: 0
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/CVPR.2008.4587586
Event Name: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Event Place: Anchorage, AK, USA

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Note: Best paper award
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5070,
  title = {Beyond Sliding Windows: Object Localization by Efficient Subwindow Search},
  author = {Lampert, CH. and Blaschko, MB. and Hofmann, T.},
  journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008)},
  booktitle = {CVPR 2008},
  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},
  month_numeric = {6}
}