Classification with scattering operators

Joan Bruna, Stéphane Mallat

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

Abstract

A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex modulus. This scattering representation is locally translation invariant and linearizes deformations. A supervised classification algorithm is computed with a PCA model selection on scattering vectors. State of the art results are obtained for handwritten digit recognition and texture classification.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages1561-1566
Number of pages6
ISBN (Print)9781457703942
DOIs
StatePublished - 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Bruna, J., & Mallat, S. (2011). Classification with scattering operators. In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 (pp. 1561-1566). [5995635] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2011.5995635