The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
Abstract
An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification. A joint translation and rotation invariant representation of image patches is calculated with a scattering transform. It is implemented with a deep convolution network, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations are calculated with linear operators in the scattering domain. State-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Cited by
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Describing Textures in the Wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., & Vedaldi, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.461 · Cited by 2,438
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2016) · arXiv · doi:10.48550/arxiv.1610.02357 · Cited by 538
Luan, S., Chen, C., Zhang, B., Han, J., & Liu, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2835143 · Cited by 428
Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A., & Weiss, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.12177 · Cited by 428
“Alternatively, one could use specially designed features such as SIFT in which invariance is hard coded [7, 29, 30, 31] or neural network architectures that explicitly enforce invariance [36, 19, 5, 6, 11, 12, 47, 46, 8].”
Method
Metadata
- Authors
- Sifre, L., & Mallat, S.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1233-1240
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2167383966
- Cited by
- 408
- References
- 2
- FWCI
- 18.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sifre, L., & Mallat, S. (2013). Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination. 2013 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2013.163
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