  This paper proposes a novel semi-supervised method on object recognition.
First, based on Boost Picking, a universal algorithm, Boost Picking Teaching
(BPT), is proposed to train an effective binary-classifier just using a few
labeled data and amounts of unlabeled data. Then, an ensemble strategy is
detailed to synthesize multiple BPT-trained binary-classifiers to be a
high-performance multi-classifier. The rationality of the strategy is also
analyzed in theory. Finally, the proposed method is tested on two databases,
CIFAR-10 and CIFAR-100. Using 2% labeled data and 98% unlabeled data, the
accuracies of the proposed method on the two data sets are 78.39% and 50.77%
respectively.
