Dynamic texture descriptors

Dynamic textures (DT) or temporal textures are textures with motion, extending spatial domain texture to the temporal domain. Dynamic texture analysis methods provide effective tools for motion analysis.  Potential applications include remote monitoring and various type of surveillance in challenging environments, such as monitoring forest fires to prevent natural disasters, traffic monitoring, homeland security applications, and animal behavior for scientific studies. DT methods are also used for  analysis of facial dynamics, recognition of actions,  video synthesis, motion segmentation, and video classification. CMVS has done extensive research on spatiotemporal dynamic texture descriptors, DT recognition, segmentation, and synthesis. Among the application areas are face analysis and recognition of actions.

Selected References

Zhao G & Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6):915-928.

Zhao G & Pietikäinen M (2009) Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. Pattern Recognition Letters 30(12):1117-1127 .

Kellokumpu V, Zhao G & Pietikäinen M (2011) Recognition of human actions using texture descriptors. Machine Vision and Applications 22(5):767-780.

Chen J, Zhao G, Kellokumpu V & Pietikäinen M (2011) Combining sparse and dense descriptors with temporal semantic structures for robust human action recognition. Proc. ICCV Workshops (VECTaR2011), Barcelona, Spain, 1524-1531.

Huang X, Zhao G, Pietikäinen M & Zheng W (2012) Spatiotemporal local monogenic binary patterns for facial expression recognition. IEEE Signal Processing Letters 19(5):243-246.

Zhao G, Ahonen T, Matas J & Pietikäinen M (2012) Rotation-invariant image and video description with local binary pattern features. IEEE Transactions on Image Processing 21(4):1465-1467.

Chen J, Zhao G, Salo M, Rahtu E & Pietikäinen M (2013) Automatic dynamic texture segmentation using local descriptors and optical flow. IEEE Transactions on Image Processing 22(1):326-339.

Guo Y, Zhao G, Zhou Z & Pietikäinen M (2013) Video texture synthesis with multi-frame LBP-TOP and diffeomorphic growth model. IEEE Transactions on Image Processing  22(10):3879-3891.

Qi X, Li C-G, Zhao G, Hong X & Pietikäinen M (2016) Dynamic texture and scene classification by transferring deep image features. Neurocomputing 171:1230-1241.

Last updated: 22.8.2016