Streaming spatio-temporal video segmentation using Gaussian Mixture Model

@article{Mukherjee2014StreamingSV,
  title={Streaming spatio-temporal video segmentation using Gaussian Mixture Model},
  author={Dibyendu Mukherjee and Q. M. Jonathan Wu},
  journal={2014 IEEE International Conference on Image Processing (ICIP)},
  year={2014},
  pages={4388-4392}
}
Development of an automatic streaming video segmentation method is crucial for many video analysis applications. However, consistency of temporal segmentation and scalability for real-time applications are difficult to achieve. This work proposes a linear-time video segmentation method which is scalable and temporally consistent for streaming videos. A Gaussian Mixture Model (GMM) is used to segment each frame while a recursive filtering updates the parameters of the GMM. This hybrid… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 14 references

Temporally consistent multi-class video-object segmentation with the Video Graph-Shifts algorithm

2011 IEEE Workshop on Applications of Computer Vision (WACV) • 2011
View 5 Excerpts
Highly Influenced

Efficient hierarchical graph-based video segmentation

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition • 2010
View 18 Excerpts
Highly Influenced

Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification

IEEE Transactions on Medical Imaging • 2008
View 13 Excerpts
Highly Influenced

Evaluation of super-voxel methods for early video processing

2012 IEEE Conference on Computer Vision and Pattern Recognition • 2012
View 3 Excerpts

ficient hierarchical graph - based video segmentation

V. Kwatra M. Grundmann, Mei Han, I. Essa
2010

Video object segmentation by tracking regions

2009 IEEE 12th International Conference on Computer Vision • 2009
View 1 Excerpt

A Topological Approach to Hierarchical Segmentation using Mean Shift

2007 IEEE Conference on Computer Vision and Pattern Recognition • 2007
View 2 Excerpts

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