Abstract
Most existing hashing methods resort to binary codes
for similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough
capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose an
asymmetric multi-valued hashing method supported by two
different non-binary embeddings. (1) A real-valued embedding is used for representing the newly-coming query. (2) A
multi-integer-embedding is employed for compressing the
whole database, which is modeled by binary sparse representation with fixed sparsity. With these two non-binary
embeddings, the similarities between data points can be preserved precisely. To perform meaningful asymmetric similarity computation for efficient semantic search, these embeddings are jointly learnt by preserving the label-based
similarity. Technically, this results in a mixed integer programming problem, which is efficiently solved by alternative optimization. Extensive experiments on three multilabel datasets demonstrate that our approach not only outperforms the existing binary hashing methods in search accuracy, but also retains their query and storage efficiency