One significant challenge in the
construction of visual detection systems is the acquisition of sufficient
labeled data. This paper describes a new technique for training visual
detectors which requires only a small quantity of labeled data, and then
uses unlabeled data to improve performance over time. Unsupervised
improvement is based on
the co-training framework of Blum and Mitchell, in which two disparate classifiers are trained simultaneously. Unlabeled examples which are confidently labeled by one classifier are added, with labels, to the training set of the other classifier. Experiments are presented on the realistic task of automobile detection in roadway
surveillance video. In this application, co-training reduces the false positive rate by a factor of 2 to 11 from the classifier trained with labeled data alone.