PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval
Christoph Lampert
In: International Conference on Computer Vision(2009).


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), that 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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6340
Deposited By:Christoph Lampert
Deposited On:08 March 2010