  Many computer vision applications involve modeling complex spatio-temporal
patterns in high-dimensional motion data. Recently, restricted Boltzmann
machines (RBMs) have been widely used to capture and represent spatial patterns
in a single image or temporal patterns in several time slices. To model global
dynamics and local spatial interactions, we propose to theoretically extend the
conventional RBMs by introducing another term in the energy function to
explicitly model the local spatial interactions in the input data. A learning
method is then proposed to perform efficient learning for the proposed model.
We further introduce a new method for multi-class classification that can
effectively estimate the infeasible partition functions of different RBMs such
that RBM is treated as a generative model for classification purpose. The
improved RBM model is evaluated on two computer vision applications: facial
expression recognition and human action recognition. Experimental results on
benchmark databases demonstrate the effectiveness of the proposed algorithm.
