Large graph hashing with spectral rotation

Faced with the requirements of huge amounts of data processing nowadays, hashing techniques have attracted much attention due to their efficient storage and searching ability. Among these techniques, the ones based on spectral graph show remarkable performance as they could embed the data on a low-dimensional manifold and maintain the neighborhood structure via a non-linear spectral eigenmap. However, the spectral solution in real value of such methods may deviate from the discrete solution. The common practice is just performing a simple rounding operation to obtain the final binary codes, which could break constraints and even result in worse condition. In this paper, we propose to impose a so-called spectral rotation technique to the spectral hashing objective, which could transform the candidate solution into a new one that better approximates the discrete one. Moreover, the binary codes are obtained from the modified solution via minimizing the Euclidean distance, which could result in more semantical correlation within the manifold, where the constraints for codes are always held. We provide an efficient alternative algorithm to solve the above problems. And a manifold learning perceptive for motivating the proposed method is also shown. Extensive experiments are conducted on three large-scale benchmark datasets and the results show our method outperforms state-of-the-art hashing methods, especially the spectral graph ones.