  Recognizing actions from still images is popularly studied recently. In this
paper, we model an action class as a flexible number of spatial configurations
of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we
discover a set of parts in image collections via unsupervised learning. Then,
our new spatial SPN is applied to model the spatial relationship and also the
high-order correlations of parts. To learn robust networks, we further develop
a hierarchical spatial SPN method, which models pairwise spatial relationship
between parts inside sub-images and models the correlation of sub-images via
extra layers of SPN. Our method is shown to be effective on two benchmark
datasets.
