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.
@article{arxiv.1511.05292,
title = {Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images},
author = {Jinghua Wang and Gang Wang},
journal= {arXiv preprint arXiv:1511.05292},
year = {2016}
}