Hashing has become a popular technique for fast similarity search in massive databases. However, learning compact binary codes which accelerates similarity search remains a challenge. In order to reduce the computational complexity of mapping semantically similar data to close codes, an anchor graph hashing (AGH) approach is used to build an approximate neighborhood graph using anchor graph, which allows constant time hashing of a new data point by extrapolating graph Laplacian eigenvectors to eigenfunctions. However, the Euclidean distance is mostly used to construct the anchor graph Laplacian in AGH, which may not reflect the semantic similarity of the data points. In this paper, we propose a method to directly optimize the anchor graph Laplacian directly based on the label smoothness and the rank pairwise loss function in the hash function learning process. The learned anchor graph, which represents the semantic similarity better between data points and anchors, is then applied to AGH for effective binary code learning. Extensive simulations are conducted on the publicly available dataset to demonstrate the effectiveness of the proposed approach.
['Haifeng Hu', 'Li Zhu', 'Jiansheng Wu']
Anchor graph hashing with semantically consistent graph