Classifying video with kernel dynamic textures
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Publication status | Published - 2007 |
Externally published | Yes |
Publication series
Name | |
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ISSN (Print) | 1063-6919 |
Conference
Title | 4th Joint IEEE International Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS'07) |
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Place | United States |
City | Minneapolis, MN |
Period | 17 - 22 June 2007 |
Link(s)
Abstract
The dynamic texture is a stochastic video model that treats the video as a sample from a linear dynamical system. The simple model has been shown to be surprisingly useful in domains such as video synthesis, video segmentation, and video classification. However, one major disadvantage of the dynamic texture is that it can only model video where the motion is smooth, i.e. video textures where the pixel values change smoothly. In this work, we propose an extension of the dynamic texture to address this issue. Instead of learning a linear observation function with PCA, we learn a non-linear observation function using kernel-PCA. The resulting kernel dynamic texture is capable of modeling a wider range of video motion, such as chaotic motion (e.g. turbulent water) or camera motion (e.g. panning). We derive the necessary steps to compute the Martin distance between kernel dynamic textures, and then validate the new model through classification experiments on video containing camera motion. © 2007 IEEE.
Citation Format(s)
Classifying video with kernel dynamic textures. / Chan, Antoni B.; Vasconcelos, Nuno.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2007.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2007.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review