Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Training products of experts by minimizing contrastive divergence
Neural Computation
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Estimation of Non-Normalized Statistical Models by Score Matching
The Journal of Machine Learning Research
Topographic Independent Component Analysis
Neural Computation
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Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a principled model for whole images, unlike ICA, which can in practice be estimated for small patches only. However, learning the filters in an MRF paradigm has been problematic in the past since it required computationally expensive Monte Carlo methods. Here, we show how MRF potentials can be estimated using Score Matching (SM). With this estimation method we can learn filters of size 12 ×12 pixels, considerably larger than traditional "hand-crafted" MRF potentials. We analyze the tuning properties of the filters in comparison to ICA filters, and show that the optimal MRF potentials are similar to the filters from an overcomplete ICA model.