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Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval

2009

Conference Paper

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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.

Author(s): Lampert, CH.
Book Title: ICCV 2009
Journal: Proceedings of the Twelfth IEEE International Conference on Computer Vision (ICCV 2009)
Pages: 987-994
Year: 2009
Month: October
Day: 0
Publisher: IEEE Computer Society

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

DOI: 10.1109/ICCV.2009.5459359
Event Name: Twelfth IEEE International Conference on Computer Vision
Event Place: Kyoto, Japan

Address: Piscataway, NJ, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6336,
  title = {Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval},
  author = {Lampert, CH.},
  journal = {Proceedings of the Twelfth IEEE International Conference on Computer Vision (ICCV 2009)},
  booktitle = {ICCV 2009},
  pages = {987-994},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = oct,
  year = {2009},
  month_numeric = {10}
}