Abstract: In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.
Abstract: A new class of percolation model in complex networks,
in which nodes are characterized by hidden variables reflecting the
properties of nodes and the occupied probability of each link is
determined by the hidden variables of the end nodes, is studied
in this paper. By the mean field theory, the analytical expressions
for the phase of percolation transition is deduced. It is determined
by the distribution of the hidden variables for the nodes and the
occupied probability between pairs of them. Moreover, the analytical
expressions obtained are checked by means of numerical simulations
on a particular model. Besides, the general model can be applied
to describe and control practical diffusion models, such as disease
diffusion model, scientists cooperation networks, and so on.