Due to the emergence and development of big data, cross-modal hash retrieval has become progressively more important in large-scale multi-modal retrieval tasks depending on its accuracy and efficiency. It completes the retrieval task in a common low-dimensional space by finding a common semantic space for heterogeneous data of different modalities. Recently, many works have concentrated on supervised cross-modal hashing and achieved higher retrieval accuracy. However, there are still many challenges in how to maintain the local geometric structure of the original space in the public space and how to use the supervision information efficiently. To deal with such issues, this paper proposes a hash retrieval method that incorporates supervised information based on matrix factorization (LCSMFH) by maintain the inter-modal and the intra-modal similarity in the original space and make the most of the label information to improve the retrieval task effect. Through experiments on two benchmark data sets, our method is more effective and outperforms state-of-the-art methods.

由于大数据的出现和发展，跨模式哈希检索在大规模多模式检索任务中变得越来越重要，这取决于其准确性和效率。它通过为不同模态的异构数据找到公共语义空间来完成在公共低维空间中的检索任务。近来，许多工作都集中在有监督的跨模式散列上，并获得了更高的检索精度。然而，如何在公共空间中保持原始空间的局部几何结构以及如何有效利用监督信息仍然存在许多挑战。为了解决这些问题，本文提出了一种散列检索方法，该方法通过在原始空间中保持模态间和模态内相似性并充分利用标签信息来提高基于矩阵的因子分解（LCSMFH）的监督信息，从而提高检索任务的效果。通过在两个基准数据集上进行的实验，我们的方法更加有效并且优于最新方法。