Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images
This work addresses the problem of recognizing human actions from static images for computer vision applications, presenting an incremental improvement in modeling spatial relationships.
The paper tackles action recognition in still images by modeling actions as spatial configurations of body parts using a hierarchical spatial Sum-Product Network (SPN), achieving effectiveness on two benchmark datasets.
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.