Title :
Classification with scattering operators
Author :
Bruna, Joan ; Mallat, Stéphane
Author_Institution :
CMAP, Ecole Polytech., Palaiseau, France
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.
Keywords :
handwritten character recognition; image classification; image texture; learning (artificial intelligence); principal component analysis; wavelet transforms; PCA model selection; complex modulus; handwritten digit recognition; locally translation invariant; multidirection co-occurrence information; multiscale co-occurrence information; scattering operators; scattering representation; scattering vector; supervised classification algorithm; texture classification; wavelet decompositions; Convolution; Databases; Principal component analysis; Scattering; Training; Wavelet transforms;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4577-0394-2
DOI :
10.1109/CVPR.2011.5995635
