Detecting objects in large image collections and videos by efficient subimage retrieval Conference Paper


Author(s): Lampert, Christoph
Title: Detecting objects in large image collections and videos by efficient subimage retrieval
Affiliation
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.
Conference Title: ICCV: International Conference on Computer Vision
Conference Dates: September 29 - October 2, 2009
Conference Location: Kyoto, Japan
Publisher: IEEE  
Location: Piscataway, NJ, USA
Date Published: 2009-09-29
Start Page: 987
End Page: 994
DOI: 10.1109/ICCV.2009.5459359
Notes: Conference Information URL: http://www.iccv2009.org/
Open access: no
IST Austria Authors
  1. Christoph Lampert
    88 Lampert
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