Multimodal hashing has attracted much interest for cross-modal similarity search on large-scale multimedia data sets because of its efficiency and effectiveness. Recently, supervised multimodal hashing, which tries to preserve the semantic information obtained from the labels of training data, has received considerable attention for its higher search accuracy compared with unsupervised multimodal hashing. Although these algorithms are promising, they are mainly designed to preserve pairwise similarities. When semantic labels of training data are given, the algorithms often transform the labels into pairwise similarities, which gives rise to the following problems: (1) constructing pairwise similarity matrix requires enormous storage space and a large amount of calculation, making these methods unscalable to large-scale data sets; (2) transforming labels into pairwise similarities loses the category information of the training data. Therefore, these methods do not enable the hash codes to preserve the discriminative information reflected by labels and, hence, the retrieval accuracies of these methods are affected. To address these challenges, this paper introduces a simple yet effective supervised multimodal hashing method, called label consistent matrix factorization hashing (LCMFH), which focuses on directly utilizing semantic labels to guide the hashing learning procedure. Considering that relevant data from different modalities have semantic correlations, LCMFH transforms heterogeneous data into latent semantic spaces in which multimodal data from the same category share the same representation. Therefore, hash codes quantified by the obtained representations are consistent with the semantic labels of the original data and, thus, can have more discriminative power for cross-modal similarity search tasks. Thorough experiments on standard databases show that the proposed algorithm outperforms several state-of-the-art methods.

中文翻译：

---

为大规模跨模式相似性搜索标记一致矩阵分解散列


多模态哈希因其高效性和有效性而引起了大规模多媒体数据集跨模态相似性搜索的极大兴趣。最近，监督多模态哈希试图保留从训练数据标签获得的语义信息，与无监督多模态哈希相比，其搜索精度更高，因此受到了广泛关注。尽管这些算法很有前景，但它们主要是为了保留成对相似性而设计的。当给定训练数据的语义标签时，算法通常将标签转换为成对相似度，这会产生以下问题：（1）构建成对相似度矩阵需要巨大的存储空间和大量的计算量，使得这些方法无法扩展到大规模数据集； （2）将标签转化为成对相似度会丢失训练数据的类别信息。因此，这些方法不能使散列码保留标签反映的判别信息，从而影响这些方法的检索精度。为了解决这些挑战，本文介绍了一种简单而有效的监督多模态哈希方法，称为标签一致矩阵分解哈希（LCMFH），其重点是直接利用语义标签来指导哈希学习过程。考虑到来自不同模态的相关数据具有语义相关性，LCMFH 将异构数据转换为潜在语义空间，其中来自同一类别的多模态数据共享相同的表示。 因此，通过获得的表示量化的哈希码与原始数据的语义标签一致，因此对于跨模态相似性搜索任务可以具有更强的判别力。对标准数据库的彻底实验表明，所提出的算法优于几种最先进的方法。