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arXiv · 1603.07957

Object Recognition Based on Amounts of Unlabeled Data

Fuqiang Liu·Fukun Bi·Liang Chen
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Abstract

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.

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Fuqiang Liu, Fukun Bi, Liang Chen. 2016-03-25. Object Recognition Based on Amounts of Unlabeled Data. https://arxiv.org/abs/1603.07957

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