Supervised cross-modal hashing without relaxation

Recently, hashing based approximate nearest neighbor search has attracted much attention in large scale data search task. Moreover, some cross-modal hashing methods have also been proposed to perform efficient search of different modalities. However, there are still some problems to be further considered. For example, some of them cannot make use of label information, which contains helpful information to generate hash codes; some of them firstly relax binary constraints during optimization, then threshold continuous outputs to binary, which could generate large quantization error. To consider these problems, in this paper, we propose a supervised cross-modal hashing without relaxation (SCMH-WR). It can not only make use of label information, but also generate the final binary codes directly, i.e., without relaxing binary constraints. Specifically, it maps different modalities into a common low-dimension subspace with preserving the similarity of labels; at the same time, it learns a rotation matrix to minimize the quantization error and gets the final binary codes. In addition, an iterative algorithm is proposed to tackle the optimization problem. SCMH-WR is tested on three benchmark data sets. Experimental results demonstrate that SCMH-WR outperforms state-of-the-art hashing methods for cross-modal search task.