  This paper presents a novel hierarchical spatiotemporal orientation
representation for spacetime image analysis. It is designed to combine the
benefits of the multilayer architecture of ConvNets and a more controlled
approach to spacetime analysis. A distinguishing aspect of the approach is that
unlike most contemporary convolutional networks no learning is involved;
rather, all design decisions are specified analytically with theoretical
motivations. This approach makes it possible to understand what information is
being extracted at each stage and layer of processing as well as to minimize
heuristic choices in design. Another key aspect of the network is its recurrent
nature, whereby the output of each layer of processing feeds back to the input.
To keep the network size manageable across layers, a novel cross-channel
feature pooling is proposed. The multilayer architecture that results
systematically reveals hierarchical image structure in terms of multiscale,
multiorientation properties of visual spacetime. To illustrate its utility, the
network has been applied to the task of dynamic texture recognition. Empirical
evaluation on multiple standard datasets shows that it sets a new
state-of-the-art.
