Classification with Invariant Scattering Representations

Details Classification with Invariant Scattering Representations Classification with Invariant Scattering Representations

2011-12-05

arxiv.org/abs/1112.1120v1

Computer Science

Computer Vision and Pattern Recognition

6 pages, 2 figures; IVMSP Workshop, 2011 IEEE 10th

Scientific paper

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 selection. State of the art results are obtained for handwritten digit recognition over small training sets, and for texture classification.

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