  In this paper, we investigate a novel reconfigurable part-based model, namely
And-Or graph model, to recognize object shapes in images. Our proposed model
consists of four layers: leaf-nodes at the bottom are local classifiers for
detecting contour fragments; or-nodes above the leaf-nodes function as the
switches to activate their child leaf-nodes, making the model reconfigurable
during inference; and-nodes in a higher layer capture holistic shape
deformations; one root-node on the top, which is also an or-node, activates one
of its child and-nodes to deal with large global variations (e.g. different
poses and views). We propose a novel structural optimization algorithm to
discriminatively train the And-Or model from weakly annotated data. This
algorithm iteratively determines the model structures (e.g. the nodes and their
layouts) along with the parameter learning. On several challenging datasets,
our model demonstrates the effectiveness to perform robust shape-based object
detection against background clutter and outperforms the other state-of-the-art
approaches. We also release a new shape database with annotations, which
includes more than 1500 challenging shape instances, for recognition and
detection.
