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

Lampert, C. (2009). Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval. In Twelfth IEEE International Conference on Computer Vision (ICCV 2009) (pp. 987-994). Piscataway, NJ, USA: IEEE Computer Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C27E-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-E34D-8
Genre: Conference Paper

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 Creators:
Lampert, CH1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2009-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/ICCV.2009.5459359
BibTex Citekey: 6336
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Title: Twelfth IEEE International Conference on Computer Vision (ICCV 2009)
Place of Event: Kyoto, Japan
Start-/End Date: 2009-09-29 - 2009-10-02

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Title: Twelfth IEEE International Conference on Computer Vision (ICCV 2009)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 987 - 994 Identifier: ISBN: 978-1-4244-4419-9