---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Christoph Lampert
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.1109/ICCV.2009.5459359
  dct_date: 2009^xs_gYear
  dct_publisher: IEEE@
  dct_title: Detecting objects in large image collections and videos by efficient
    subimage retrieval@
...