  Hierarchies allow feature sharing between objects at multiple levels of
representation, can code exponential variability in a very compact way and
enable fast inference. This makes them potentially suitable for learning and
recognizing a higher number of object classes. However, the success of the
hierarchical approaches so far has been hindered by the use of hand-crafted
features or predetermined grouping rules. This paper presents a novel framework
for learning a hierarchical compositional shape vocabulary for representing
multiple object classes. The approach takes simple contour fragments and learns
their frequent spatial configurations. These are recursively combined into
increasingly more complex and class-specific shape compositions, each exerting
a high degree of shape variability. At the top-level of the vocabulary, the
compositions are sufficiently large and complex to represent the whole shapes
of the objects. We learn the vocabulary layer after layer, by gradually
increasing the size of the window of analysis and reducing the spatial
resolution at which the shape configurations are learned. The lower layers are
learned jointly on images of all classes, whereas the higher layers of the
vocabulary are learned incrementally, by presenting the algorithm with one
object class after another. The experimental results show that the learned
multi-class object representation scales favorably with the number of object
classes and achieves a state-of-the-art detection performance at both, faster
inference as well as shorter training times.
