Abstract
Hashing-based method has been widely used as the approximate nearest neighbor search model. Kernel based hashing method is a representative hashing-based method because it can make full use of supervised information. It can achieve good performance under the case that the supervised information is sufficient. However, its performance would be influenced with limited label information. In order to deal with this case, we propose a novel semi-supervised hashing method based on label propagation. The proposed method first transfers the labeled information based on graph transduction from the label samples to unlabeled samples, and then constructs a new label matrix. The proposed method can efficiently use the label information to further improve its performance. Extensive experimental results verify the validity of the proposed method.
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Wang, X., Gao, X., Li, J., Wang, Y. (2013). An Efficient Semi-supervised Hashing Method Based on Graph Transduction. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_72
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DOI: https://doi.org/10.1007/978-3-642-42057-3_72
Publisher Name: Springer, Berlin, Heidelberg
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