We present Context Forest (ConF), a technique for predicting properties of
the objects in an image based on its global appearance. Compared to standard
nearest-neighbour techniques, ConF is more accurate, fast and memory efficient...
We train ConF to predict which aspects of an object class are likely to appear
in a given image (e.g. which viewpoint). This enables to speed-up
multi-component object detectors, by automatically selecting the most relevant
components to run on that image. This is particularly useful for detectors
trained from large datasets, which typically need many components to fully
absorb the data and reach their peak performance. ConF provides a speed-up of
2x for the DPM detector  and of 10x for the EE-SVM detector . To show
ConF's generality, we also train it to predict at which locations objects are
likely to appear in an image. Incorporating this information in the detector
score improves mAP performance by about 2% by removing false positive
detections in unlikely locations.