Oliver Williams, Andrew Blake, and Roberto Cipolla
There has been substantial progress in the past decade in the development of object classifiers for images, for example of faces, humans and vehicles. 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 spatial coherence prior. Variational inference is used to marginalize over contamination and obtain robust classification. In this way the VIC approach can turn a kernel classifier for clean data into one that can tolerate contamination, without any specific training on contaminated positives.
|Published in||Advances in Neural Information Processing Systems 17|
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Oliver Williams. Bayesian Learning for Efficient Visual Inference, September 2005.