Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
Computer Vision and Pattern Recognition
2012-07-03 v1 Machine Learning
Machine Learning
Abstract
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial "sparse" methodology. We derive a large scale approximate Bayesian inference algorithm for linear models with non-factorial (latent tree-structured) scale mixture priors. Experimental results on a range of denoising and inpainting problems demonstrate substantially improved performance compared to MAP estimation or to inference with factorial priors.
Cite
@article{arxiv.1206.6437,
title = {Large Scale Variational Bayesian Inference for Structured Scale Mixture Models},
author = {Young Jun Ko and Matthias Seeger},
journal= {arXiv preprint arXiv:1206.6437},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)