The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deformation Models for Image Recognition
Abstract
We present the application of different nonlinear image deformation models to the task of image recognition. The deformation models are especially suited for local changes as they often occur in the presence of image object variability. We show that, among the discussed models, there is one approach that combines simplicity of implementation, low-computational complexity, and highly competitive performance across various real-world image recognition tasks. We show experimentally that the model performs very well for four different handwritten digit recognition tasks and for the classification of medical images, thus showing high generalization capacity. In particular, an error rate of 0.54 percent on the MNIST benchmark is achieved, as well as the lowest reported error rate, specifically 12.6 percent, in the 2005 international ImageCLEF evaluation of medical image categorization.
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Cited by
Invariant Scattering Convolution Networks
Bruna, J., & Mallat, S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.230 · Cited by 1,633
Koller, O., Forster, J., & Ney, H. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.09.013 · Cited by 533
Self-citationA Survey of Handwritten Character Recognition with MNIST and EMNIST
Baldominos, A., Saez, Y., & Isasi, P. (2019) · Applied Sciences · doi:10.3390/app9153169 · Cited by 279
Fast hierarchical clustering of local density peaks via an association degree transfer method
Guan, J., Li, S., He, X., Zhu, J., & Chen, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.071 · Cited by 73
Metadata
- Authors
- Keysers, D., Deselaers, T., Gollan, C., & Ney, H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-08
- Volume
- 29
- Issue
- 8
- Pages
- 1422-1435
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2135833910
- Cited by
- 255
- References
- 3
- FWCI
- 18.33
- Type
- journal article
- Language
- en
- Open access
- closed
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- Keysers, D., Deselaers, T., Gollan, C., & Ney, H. (2007). Deformation Models for Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.1153
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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