PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating Markov Random Field Potentials for Natural Images
Urs Köster, Jussi T. Lindgren and Aapo Hyvärinen
In: ICA 2009, 15-18 Mar 2009, Paraty RJ / Brazil.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4772
Deposited By:Jussi Lindgren
Deposited On:24 March 2009