---
_id: '3710'
abstract:
- lang: eng
text: 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 estimate the object‘s location, one can take a
sliding window approach, but this strongly increases the computational cost because
the classifier or similarity function has to be evaluated over a large set of
candidate subwindows. In this paper, we propose a simple yet powerful branch and
bound scheme that allows efficient maximization of a large class of quality functions
over all possible subimages. It converges to a globally optimal solution typically
in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive
or sliding window search. We show how our method is applicable to different object
detection and image 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 chi^2 distance. We demonstrate state-of-the-art localization performance
of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set,
and in the PASCAL VOC 2007 competition.
acknowledgement: 'This work was funded in part by the EU projects CLASS, IST 027978,
and PerAct, EST 504321. '
author:
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
- first_name: Thomas
full_name: Hofmann,Thomas
last_name: Hofmann
citation:
ama: 'Lampert C, Blaschko M, Hofmann T. Efficient subwindow search: A branch and
bound framework for object localization. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 2009;31(12):2129-2142. doi:10.1109/TPAMI.2009.144'
apa: 'Lampert, C., Blaschko, M., & Hofmann, T. (2009). Efficient subwindow search:
A branch and bound framework for object localization. IEEE Transactions on
Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2009.144'
chicago: 'Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Efficient Subwindow
Search: A Branch and Bound Framework for Object Localization.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2009. https://doi.org/10.1109/TPAMI.2009.144.'
ieee: 'C. Lampert, M. Blaschko, and T. Hofmann, “Efficient subwindow search: A branch
and bound framework for object localization,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 31, no. 12. IEEE, pp. 2129–2142, 2009.'
ista: 'Lampert C, Blaschko M, Hofmann T. 2009. Efficient subwindow search: A branch
and bound framework for object localization. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 31(12), 2129–2142.'
mla: 'Lampert, Christoph, et al. “Efficient Subwindow Search: A Branch and Bound
Framework for Object Localization.” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 31, no. 12, IEEE, 2009, pp. 2129–42, doi:10.1109/TPAMI.2009.144.'
short: C. Lampert, M. Blaschko, T. Hofmann, IEEE Transactions on Pattern Analysis
and Machine Intelligence 31 (2009) 2129–2142.
date_created: 2018-12-11T12:04:45Z
date_published: 2009-12-01T00:00:00Z
date_updated: 2021-01-12T07:51:39Z
day: '01'
doi: 10.1109/TPAMI.2009.144
extern: 1
intvolume: ' 31'
issue: '12'
main_file_link:
- open_access: '0'
url: http://www2.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2009.144
month: '12'
page: 2129 - 2142
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '2648'
quality_controlled: 0
status: public
title: 'Efficient subwindow search: A branch and bound framework for object localization'
type: journal_article
volume: 31
year: '2009'
...