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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.
