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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 fewlabeled data and amounts of unlabeled data. Then, an ensemble strategy isdetailed to synthesize multiple BPT-trained binary-classifiers to be ahigh-performance multi-classifier. The rationality of the strategy is alsoanalyzed in theory. Finally, the proposed method is tested on two databases,CIFAR-10 and CIFAR-100. Using 2% labeled data and 98% unlabeled data, theaccuracies of the proposed method on the two data sets are 78.39% and 50.77%respectively.

Object Recognition Based on Amounts of Unlabeled Data