Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
Learning invariance from transformation sequences
Neural Computation
Convergent algorithm for sensory receptive field development
Neural Computation
Unsupervised learning
Learning Lie groups for invariant visual perception
Proceedings of the 1998 conference on Advances in neural information processing systems II
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning in neural computation
Theoretical Computer Science - Natural computing
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A minimum description length framework for unsupervised learning
A minimum description length framework for unsupervised learning
Learning invariant object recognition in the visual system with continuous transformations
Biological Cybernetics
Learning the Lie Groups of Visual Invariance
Neural Computation
Neural Computation
| Hi-index | 0.00 |
The use of image transformations is essential for efficient modeling and learning of visual data. But the class of relevant transformations is large: affine transformations, projective transformations, elastic deformations, ... the list goes on. Therefore, learning these transformations, rather than hand coding them, is of great conceptual interest. To the best of our knowledge, all the related work so far has been concerned with either supervised or weakly supervised learning (from correlated sequences, video streams, or image-transform pairs). In this paper, on the contrary, we present a simple method for learning affine and elastic transformations when no examples of these transformations are explicitly given, and no prior knowledge of space (such as ordering of pixels) is included either. The system has only access to a moderately large database of natural images arranged in no particular order.