Sparse Graph Hashing with Spectral Regression

Learning-based hashing has received increasing research attention due to its promising efficiency for large-scale similarity search. However, most existing manifold-based hashing methods cannot capture the intrinsic structure and discriminative information of image samples. In this paper, we propose a new learning-based hashing method, namely, Sparse Graph Hashing with Spectral Regression (SGHSR), for approximate nearest neighbor search. We first propose a sparse graph model to learn the real-valued codes which can not only preserves the manifold structure of the data, but also adaptively selects sparse and discriminative features. Then, we use a spectral regression to convert the real-valued codes into high-quality binary codes such that the information loss between the original space and the Hamming space can be well minimized. Extensive experimental results on three widely used image databases demonstrate that our SGHSR method outperforms the state-of-the-art unsupervised manifold-based hashing methods.