Efficient semi-supervised annotation with Proxy-based Local Consistency Propagation

Semi-supervised learning methods can largely leverage the image annotation problem using both labeled and unlabeled data, especially when the labeled information is quite limited. However, most of them suffer the expensive computation stemming from the batch learning on large training dataset. In this paper we proposed a highly efficient semi-supervised annotation approach with the partial label propagation based on the graph representation. Specifically, the label information is first propagated from labeled samples to the unlabeled ones, and then spreads only among unlabeled ones like a spreading activation network. Our approach takes advantage of the decomposed formulation to achieve a fast incremental learning instead of the expensive batch one without accuracy loss. Extensive evaluations over two large datasets demonstrate the superior performance of the proposed method and its significant efficiency.