The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data

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.

在过去十年中，图像目标分类器（例如面部、人体和车辆的分类器）的开发取得了重大进展。在这里，我们要解决测试图像中存在的污染问题（例如遮挡、阴影），这些问题在训练数据中并未明确出现过。变分伊辛分类器（VIC）算法将污染建模为一个具有强空间相干先验的掩码（一个二元变量场）。变分推理用于对污染进行边缘化处理并获得稳健的分类结果。通过这种方式，VIC方法可以将一个用于干净数据的核分类器转变为一个能够容忍污染的分类器，而无需对受污染的正样本进行任何特定的训练。