Dynamic texture analysis with diffusion in networks

Author(s)

Ribas, Lucas C. ^{[1, 2]} ; Goncalves, Wesley N. ^[3] ; Bruno, Odemir M. ^{[1, 2]}

Affiliation

^[1] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[3] Univ Fed Mato Grosso do Sul, Ponta Pora, MS - Brazil

• Document type: Journal article
• Source: DIGITAL SIGNAL PROCESSING; v. 92, p. 109-126, SEP 2019.
• Web of Science Citations: 0

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 known dynamic texture database and on traffic condition classification, and outperform in most of the cases. (C) 2019 Elsevier Inc. All rights reserved. (AU)

• FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
• Grantee: Lucas Correia Ribas
• Support type: Scholarships in Brazil - Doctorate
• FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
• Grantee: Odemir Martinez Bruno
• Support type: Regular Research Grants