Corpus ID: 220250590

Unsupervised Learning Consensus Model for Dynamic Texture Videos Segmentation

@article{Khelifi2020UnsupervisedLC,
  title={Unsupervised Learning Consensus Model for Dynamic Texture Videos Segmentation},
  author={Lazhar Khelifi and M. Mignotte},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.16177}
}
  • Lazhar Khelifi, M. Mignotte
  • Published 2020
  • Computer Science
  • ArXiv
  • Dynamic texture (DT) segmentation, and video processing in general, is currently widely dominated by methods based on deep neural networks that require the deployment of a large number of layers. Although this parametric approach has shown superior performances for the dynamic texture segmentation, all current deep learning methods suffer from a significant main weakness related to the lack of a sufficient reference annotation to train models and to make them functional. In addition, the result… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 36 REFERENCES
    AIDS and scx tourism: conclusions dra1.\"Tl from a study of the sodal and pS~'chological characlcristics of Gennan sex wurists
    • 1995
    Dependence of kidney morphogenesis on the expression of nerve growth factor receptor.
    112
    RESEARCH PAPER ON CLUSTER TECHNIQUES OF DATA VARIATIONS
    19
    Tid1/Rdh54 promotes colocalization of rad51 and dmc1 during meiotic recombination.
    175
    Learning similarity metrics for dynamic scene segmentation
    15