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Poster

A multiscale model of natural images

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84256

Theis,  L
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83982

Hosseini,  R
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Theis, L., Hosseini, R., & Bethge, M. (2011). A multiscale model of natural images. Poster presented at 12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011), Heiligkreuztal, Germany.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B9AC-B
Zusammenfassung
We present a probabilistic model for natural images which is based on Gaussian scale mixtures and a simple multiscale representation. In contrast to the dominant approach to modeling whole images focusing on Markov random fields, we formulate our model in terms of a directed graphical model. We show that it is able to generate images with interesting higher-order correlations when trained on natural images or samples from an occlusion based model. More importantly, the directed model enables us to perform a principled evaluation. While it is easy to generate visually appealing images, we demonstrate that our model also yields the best performance reported to date when evaluated with respect to the cross-entropy rate, a measure tightly linked to the average log-likelihood.