International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction of Temporal Texture Based on Spatiotemporal Motion Trajectory
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Texture analysis and classification using deterministic tourist walk
Pattern Recognition
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Deterministic tourist walks as an image analysis methodology based
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Dynamic texture segmentation based on deterministic partially self-avoiding walks
Computer Vision and Image Understanding
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Dynamic texture has been attracting extensive attention in the field of computer vision in the last years. These patterns can be described as moving textures which the idea of self-similarity presented by static textures is extended to the spatio-temporal domain. Although promising results have been achieved by recent methods, most of them cannot model multiple regions of dynamic textures and/or both motion and appearance features. To overcome these drawbacks, a novel approach for dynamic texture modeling based on deterministic partially self-avoiding walks is proposed. In this method, deterministic partially self-avoiding walks are performed in three orthogonal planes to combine appearance and motion features of the dynamic textures. Experimental results on two databases indicate that the proposed method improves correct classification rate compared to the existing methods.