Classification with invariant scattering representations

Joan Bruna, Stéphane Mallat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2011 IEEE 10th IVMSP Workshop
Subtitle of host publicationPerception and Visual Signal Analysis, IVMSP 2011 - Proceedings
PublisherIEEE Computer Society
Pages99-104
Number of pages6
ISBN (Print)9781457712869
DOIs
StatePublished - 2011

Publication series

Name2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis, IVMSP 2011 - Proceedings

Keywords

  • Image classification
  • Invariant representations
  • local image descriptors
  • pattern recognition
  • texture classification

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Bruna, J., & Mallat, S. (2011). Classification with invariant scattering representations. In 2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis, IVMSP 2011 - Proceedings (pp. 99-104). [5970362] (2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis, IVMSP 2011 - Proceedings). IEEE Computer Society. https://doi.org/10.1109/IVMSPW.2011.5970362