Dynamic texture analysis with diffusion in networks

TL;DR

This paper introduces a novel dynamic texture analysis method using diffusion in directed networks, modeling textures as networks and analyzing their activity to improve classification robustness.

Contribution

It proposes a new approach based on directed network modeling and diffusion for dynamic texture characterization, enhancing classification performance.

Findings

Outperforms existing methods on dynamic texture databases.

Demonstrates robustness to motion pattern interference.

Effective in traffic condition classification.

Abstract

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.