  This paper proposes a reconfigurable model to recognize and detect multiclass
(or multiview) objects with large variation in appearance. Compared with well
acknowledged hierarchical models, we study two advanced capabilities in
hierarchy for object modeling: (i) "switch" variables(i.e. or-nodes) for
specifying alternative compositions, and (ii) making local classifiers (i.e.
leaf-nodes) shared among different classes. These capabilities enable us to
account well for structural variabilities while preserving the model compact.
Our model, in the form of an And-Or Graph, comprises four layers: a batch of
leaf-nodes with collaborative edges in bottom for localizing object parts; the
or-nodes over bottom to activate their children leaf-nodes; the and-nodes to
classify objects as a whole; one root-node on the top for switching multiclass
classification, which is also an or-node. For model training, we present an
EM-type algorithm, namely dynamical structural optimization (DSO), to
iteratively determine the structural configuration, (e.g., leaf-node generation
associated with their parent or-nodes and shared across other classes), along
with optimizing multi-layer parameters. The proposed method is valid on
challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves
state-of-the-arts performance.
