Classification with invariant scattering representations

@article{Bruna2011ClassificationWI,
  title={Classification with invariant scattering representations},
  author={Joan Bruna and St{\'e}phane Mallat},
  journal={2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis},
  year={2011},
  pages={99-104}
}
  • Joan Bruna, Stéphane Mallat
  • Published 2011
  • Computer Science, Mathematics
  • 2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis
  • A scattering transform defines a signal representation which is invariant to translations and Lipschitz continuous relatively to deformations. It is implemented with a non-linear convolution network that iterates over wavelet and modulus operators. Lipschitz continuity locally linearizes deformations. Complex classes of signals and textures can be modeled with low-dimensional affine spaces, computed with a PCA in the scattering domain. Classification is performed with a penalized model… CONTINUE READING

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    SHOWING 1-10 OF 20 REFERENCES

    Recursive interferometric representations

    Group Invariant Scattering

    VIEW 8 EXCERPTS

    Tangent distance kernels for support vector machines

    VIEW 1 EXCERPT

    A Statistical Approach to Material Classification Using Image Patch Exemplars

    VIEW 2 EXCERPTS

    Lambertian reflectance and linear subspaces

    VIEW 1 EXCERPT

    Convolutional networks and applications in vision

    VIEW 6 EXCERPTS
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