Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CVPR.2011.5995635 | CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
scattering,learning artificial intelligence,principal component analysis,databases,image texture,image classification,wavelet transforms,convolution,model selection | Journal | 2011 |
Issue | ISSN | Citations |
1 | 1063-6919 | 13 |
PageRank | References | Authors |
1.12 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Bruna | 1 | 1697 | 82.95 |
| Stéphane Mallat | 2 | 4107 | 718.30 |