  We propose a joint object pose estimation and categorization approach which
extracts information about object poses and categories from the object parts
and compositions constructed at different layers of a hierarchical object
representation algorithm, namely Learned Hierarchy of Parts (LHOP). In the
proposed approach, we first employ the LHOP to learn hierarchical part
libraries which represent entity parts and compositions across different object
categories and views. Then, we extract statistical and geometric features from
the part realizations of the objects in the images in order to represent the
information about object pose and category at each different layer of the
hierarchy. Unlike the traditional approaches which consider specific layers of
the hierarchies in order to extract information to perform specific tasks, we
combine the information extracted at different layers to solve a joint object
pose estimation and categorization problem using distributed optimization
algorithms. We examine the proposed generative-discriminative learning approach
and the algorithms on two benchmark 2-D multi-view image datasets. The proposed
approach and the algorithms outperform state-of-the-art classification,
regression and feature extraction algorithms. In addition, the experimental
results shed light on the relationship between object categorization, pose
estimation and the part realizations observed at different layers of the
hierarchy.
