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

Lampert, CH

doi:10.1109/ICCV.2009.5459359

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

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Lampert, CH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

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Citation

Lampert, C. (2009). Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval. Proceedings of the Twelfth IEEE International Conference on Computer Vision (ICCV 2009), 987-994.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C27E-0

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.