  In this paper, we study the problem of learning a model for human pose
estimation as mixtures of compositional sub-trees in two layers of prediction.
This involves estimating the pose of a sub-tree followed by identifying the
relationships between sub-trees that are used to handle occlusions between
different parts. The mixtures of the sub-trees are learnt utilising both
geometric and appearance distances. The Chow-Liu (CL) algorithm is recursively
applied to determine the inter-relations between the nodes and to build the
structure of the sub-trees. These structures are used to learn the latent
parameters of the sub-trees and the inference is done using a standard belief
propagation technique. The proposed method handles occlusions during the
inference process by identifying overlapping regions between different
sub-trees and introducing a penalty term for overlapping parts. Experiments are
performed on three different datasets: the Leeds Sports, Image Parse and UIUC
People datasets. The results show the robustness of the proposed method to
occlusions over the state-of-the-art approaches.
