Assignment of Multiplicative Mixtures in Natural Images
Abstract
In the analysis of natural images, Gaussian scale mixtures (GSM) have been used to account for the statistics of (cid:2)lter responses, and to inspire hi- erarchical cortical representational learning schemes. GSMs pose a crit- ical assignment problem, working out which (cid:2)lter responses were gen- erated by a common multiplicative factor. We present a new approach to solving this assignment problem through a probabilistic extension to the basic GSM, and show how to perform inference in the model using Gibbs sampling. We demonstrate the ef(cid:2)cacy of the approach on both synthetic and image data.
Cite
Text
Schwartz et al. "Assignment of Multiplicative Mixtures in Natural Images." Neural Information Processing Systems, 2004.Markdown
[Schwartz et al. "Assignment of Multiplicative Mixtures in Natural Images." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/schwartz2004neurips-assignment/)BibTeX
@inproceedings{schwartz2004neurips-assignment,
title = {{Assignment of Multiplicative Mixtures in Natural Images}},
author = {Schwartz, Odelia and Sejnowski, Terrence J. and Dayan, Peter},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1217-1224},
url = {https://mlanthology.org/neurips/2004/schwartz2004neurips-assignment/}
}