  The goal of object detection is to find objects in an image. An object
detector accepts an image and produces a list of locations as $(x,y)$ pairs.
Here we introduce a new concept: {\bf location-based boosting}. Location-based
boosting differs from previous boosting algorithms because it optimizes a new
spatial loss function to combine object detectors, each of which may have
marginal performance, into a single, more accurate object detector. A
structured representation of object locations as a list of $(x,y)$ pairs is a
more natural domain for object detection than the spatially unstructured
representation produced by classifiers. Furthermore, this formulation allows us
to take advantage of the intuition that large areas of the background are
uninteresting and it is not worth expending computational effort on them. This
results in a more scalable algorithm because it does not need to take measures
to prevent the background data from swamping the foreground data such as
subsampling or applying an ad-hoc weighting to the pixels. We first present the
theory of location-based boosting, and then motivate it with empirical results
on a challenging data set.
