  Dynamic texture is a field of research that has gained considerable interest
from computer vision community due to the explosive growth of multimedia
databases. In addition, dynamic texture is present in a wide range of videos,
which makes it very important in expert systems based on videos such as medical
systems, traffic monitoring systems, forest fire detection system, among
others. In this paper, a new method for dynamic texture characterization based
on diffusion in directed networks is proposed. The dynamic texture is modeled
as a directed network. The method consists in the analysis of the dynamic of
this network after a series of graph cut transformations based on the edge
weights. For each network transformation, the activity for each vertex is
estimated. The activity is the relative frequency that one vertex is visited by
random walks in balance. Then, texture descriptor is constructed by
concatenating the activity histograms. The main contributions of this paper are
the use of directed network modeling and diffusion in network to dynamic
texture characterization. These tend to provide better performance in dynamic
textures classification. Experiments with rotation and interference of the
motion pattern were conducted in order to demonstrate the robustness of the
method. The proposed approach is compared to other dynamic texture methods on
two very well know dynamic texture database and on traffic condition
classification, and outperform in most of the cases.
