  This paper studies a novel discriminative part-based model to represent and
recognize object shapes with an "And-Or graph". We define this model consisting
of three layers: the leaf-nodes with collaborative edges for localizing local
parts, the or-nodes specifying the switch of leaf-nodes, and the root-node
encoding the global verification. A discriminative learning algorithm, extended
from the CCCP [23], is proposed to train the model in a dynamical manner: the
model structure (e.g., the configuration of the leaf-nodes associated with the
or-nodes) is automatically determined with optimizing the multi-layer
parameters during the iteration. The advantages of our method are two-fold. (i)
The And-Or graph model enables us to handle well large intra-class variance and
background clutters for object shape detection from images. (ii) The proposed
learning algorithm is able to obtain the And-Or graph representation without
requiring elaborate supervision and initialization. We validate the proposed
method on several challenging databases (e.g., INRIA-Horse, ETHZ-Shape, and
UIUC-People), and it outperforms the state-of-the-arts approaches.
