2011/12/05 by Joan Bruna, Bruna, Joan, Stéphane Mallat +1
Computer Science · Earth and Planetary Sciences · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Functional Analysis (math.FA) #Image Processing Techniques and Applications #Machine Learning (stat.ML) #Neural Networks and Applications #Seismic Imaging and Inversion Techniques
paper · pdf · doi:10.48550/arxiv.1112.1120
openalex publication_date 2011/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.