  Object detection is an important research area in the field of computer
vision. Many detection algorithms have been proposed. However, each object
detector relies on specific assumptions of the object appearance and imaging
conditions. As a consequence, no algorithm can be considered as universal. With
the large variety of object detectors, the subsequent question is how to select
and combine them.
  In this paper, we propose a framework to learn how to combine object
detectors. The proposed method uses (single) detectors like DPM, CN and EES,
and exploits their correlation by high level contextual features to yield a
combined detection list.
  Experiments on the PASCAL VOC07 and VOC10 datasets show that the proposed
method significantly outperforms single object detectors, DPM (8.4%), CN (6.8%)
and EES (17.0%) on VOC07 and DPM (6.5%), CN (5.5%) and EES (16.2%) on VOC10.
