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Matthias Bethge
(Professor of Computaional Neuroscience)
Wednesday 21st September 2011
16.00pm
B10 Seminar Room,
Alexandra House, 17 Queen Square, London, WC1N 3AR
How effective are neural model representations in capturing the higher-order correlations of natural images?
The physical structure of objects manifests itself in the higher-order correlations of natural images. The content of an image is still recognized easily if one removes all structure responsible for second-order correlation but otherwise leaves the higher-order correlations untouched. Since modeling higher-order correlations is a challenging task, it can be highly informative to examine to what extent neural model representation have the capacity to account for them. In this talk I will give an overview on the research done in my lab over the last 4-5 years in order to quantify the potential of different mechanisms such as orientation selectivity, contrast gain control, complex cell pooling or hierarchical architectures to capture these higher-order correlations. I will also present a new model---a Mixture of Conditional Gaussian Scale Mixtures---which achieves the best likelihood on natural images that has been reported to date. It also performs remarkably well as a texture synthesis algorithms demonstrating the perceptual relevance of the modeled correlations.
