The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data
Abstract
There has been substantial progress in the past decade in the development of object classifiers for images, for example of faces, humans and vehi- cles. Here we address the problem of contaminations (e.g. occlusion, shadows) in test images which have not explicitly been encountered in training data. The Variational Ising Classifier (VIC) algorithm models contamination as a mask (a field of binary variables) with a strong spa- tial coherence prior. Variational inference is used to marginalize over contamination and obtain robust classification. In this way the VIC ap- proach can turn a kernel classifier for clean data into one that can tolerate contamination, without any specific training on contaminated positives.
Cite
Text
Williams et al. "The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data." Neural Information Processing Systems, 2004.Markdown
[Williams et al. "The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/williams2004neurips-variational/)BibTeX
@inproceedings{williams2004neurips-variational,
title = {{The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data}},
author = {Williams, Oliver and Blake, Andrew and Cipolla, Roberto},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1497-1504},
url = {https://mlanthology.org/neurips/2004/williams2004neurips-variational/}
}