  We propose a method for annotating the location of objects in ImageNet.
Traditionally, this is cast as an image window classification problem, where
each window is considered independently and scored based on its appearance
alone. Instead, we propose a method which scores each candidate window in the
context of all other windows in the image, taking into account their similarity
in appearance space as well as their spatial relations in the image plane. We
devise a fast and exact procedure to optimize our scoring function over all
candidate windows in an image, and we learn its parameters using structured
output regression. We demonstrate on 92000 images from ImageNet that this
significantly improves localization over recent techniques that score windows
in isolation.
