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Invariant Scattering Convolution Networks
Aug. 2013 (vol. 35 no. 8)
pp. 1872-1886
Joan Bruna, Courant Inst., New York Univ., New York, NY, USA
S. Mallat, Ecole Normale Super., Paris, France
A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.
Index Terms:
wavelet transforms,convolution,Gaussian processes,handwritten character recognition,image classification,image representation,image texture,principal component analysis,support vector machines,generative PCA classifier,invariant scattering convolution networks,wavelet scattering network,translation invariant image representation,deformations,high-frequency information,wavelet transform convolutions,nonlinear modulus,averaging operators,network layer,SIFT-type descriptors,complementary invariant information,mathematical analysis,deep convolution networks,scattering representation,stationary process,Fourier power spectrum,state-of-the-art classification,handwritten digits,texture discrimination,Gaussian kernel SVM,Scattering,Convolution,Fourier transforms,Wavelet coefficients,Computer architecture,wavelets,Classification,convolution networks,deformations,invariants
Citation:
Joan Bruna, S. Mallat, "Invariant Scattering Convolution Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1872-1886, Aug. 2013, doi:10.1109/TPAMI.2012.230
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