TY - GEN
T1 - Classification with invariant scattering representations
AU - Bruna, Joan
AU - Mallat, Stéphane
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Image classification
KW - Invariant representations
KW - local image descriptors
KW - pattern recognition
KW - texture classification
UR - http://www.scopus.com/inward/record.url?scp=80052302973&partnerID=8YFLogxK
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U2 - 10.1109/IVMSPW.2011.5970362
DO - 10.1109/IVMSPW.2011.5970362
M3 - Conference contribution
AN - SCOPUS:80052302973
SN - 9781457712869
T3 - 2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis, IVMSP 2011 - Proceedings
SP - 99
EP - 104
BT - 2011 IEEE 10th IVMSP Workshop
PB - IEEE Computer Society
ER -