HUMAN PARSING WITH A CASCADE OF HIERARCHICAL POSELET BASED PRUNERS

We address the problem of human parsing using part-based models. In particular, we consider part-based models that exploit rich pairwise relationship between parts, e.g. the color symmetry between left/right limbs. This poses a computational challenge since the state space of each part is very large, and algorithmic tricks (e.g. the distance transform) cannot be applied to handle these types of pairwise relationships. We propose to prune the state space of each part using a cascade of pruners. These pruners can filter out 99.6% of the states per part to about 500 states per part, while keeping the ground-truth states in the pruned state most of the time. In the pruned space, we can afford to apply human parsing models with more complex pairwise relationships between parts, such as the color symmetry. We demonstrate our method on a challenging human parsing dataset.