Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2011.6126372
Title: Dynamic texture classification using dynamic fractal analysis
Authors: Xu, Y.
Quan, Y.
Ling, H.
Ji, H. 
Issue Date: 2011
Citation: Xu, Y.,Quan, Y.,Ling, H.,Ji, H. (2011). Dynamic texture classification using dynamic fractal analysis. Proceedings of the IEEE International Conference on Computer Vision : 1219-1226. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2011.6126372
Abstract: In this paper, we developed a novel tool called dynamic fractal analysis for dynamic texture (DT) classification, which not only provides a rich description of DT but also has strong robustness to environmental changes. The resulting dynamic fractal spectrum (DFS) for DT sequences consists of two components: One is the volumetric dynamic fractal spectrum component (V-DFS) that captures the stochastic self-similarities of DT sequences as 3D volume datasets; the other is the multi-slice dynamic fractal spectrum component (S-DFS) that encodes fractal structures of DT sequences on 2D slices along different views of the 3D volume. Various types of measures of DT sequences are collected in our approach to analyze DT sequences from different perspectives. The experimental evaluation is conducted on three widely used benchmark datasets. In all the experiments, our method demonstrated excellent performance in comparison with state-of-the-art approaches. © 2011 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/104560
ISBN: 9781457711015
DOI: 10.1109/ICCV.2011.6126372
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