The objective of this work is the detection of object classes, such as airplanes or horses. Instead of using a model based
on salient image fragments, we show that object class detection is also possible using only the object’s boundary. To this
end, we develop a novel learning technique to extract class-discriminative boundary fragments. In addition to their shape,
these “codebook” entries also determine the object’s centroid (in the manner of Leibe et al. [19]). Boosting is used to select discriminative combinations of boundary fragments (weak detectors) to form a strong “Boundary-Fragment-Model”
(BFM) detector. The generative aspect of the model is used to determine an approximate segmentation.
We demonstrate the following results: (i) the BFM detector is able to represent and detect object classes principally defined
by their shape, rather than their appearance; and (ii) in comparison with other published results on several object classes
(airplanes, cars-rear, cows) the BFM detector is able to exceed previous performances, and to achieve this with less supervision
(such as the number of training images).