Invariant Scattering Convolution Networks

作者：Joan Bruna, Stéphane Mallat · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2013 · DOI：10.1109/tpami.2012.230 · 被引用次数：1677 · 研究领域：Image Retrieval and Classification Techniques、Advanced Image and Video Retrieval Techniques、Medical Image Segmentation Techniques

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.