Multi-view anchor graph hashing

Multi-view hashing seeks compact integrated binary codes which preserve similarities averaged over multiple representations of objects. Most of existing multi-view hashing methods resort to linear hash functions where data manifold is not considered. In this paper we present multi-view anchor graph hashing (MVAGH), where nonlinear integrated binary codes are efficiently determined by a subset of eigenvectors of an averaged similarity matrix. The efficiency behind MVAGH is due to a low-rank form of the averaged similarity matrix induced by multi-view anchor graph, where the similarity between two points is measured by two-step transition probability through view-specific anchor (i.e. landmark) points. In addition, we observe that MVAGH suffers from the performance degradation when the high recall is required. To overcome this drawback, we propose a simple heuristic to combine MVAGH with locality sensitive hashing (LSH). Numerical experiments on CIFAR-10 dataset confirms that MVAGH(+LSH) outperforms the existing multi- and single-view hashing methods.