Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous multi-modal features at both the model training and online retrieval stages to support large-scale multimedia retrieval. Previous multi-modal hashing methods mainly focus on supervised and unsupervised hashing. The performance of supervised hashing largely relies on the number of labeled data, which is practically expensive to obtain. Unsupervised hashing methods cannot effectively capture the semantic correlations of multi-modal data without any labels for supervision. In this paper, we propose an Efficient Semi-supervised Multi-modal Hashing with Importance Differentiation Regression (ESMH-IDR) model, which can alleviate the existing problems by learning from both labeled and unlabeled data. Specifically, in this paper, we develop an efficient semi-supervised multi-modal hash code learning module. It learns the hash codes for labeled data in an efficient asymmetric way, and simultaneously performs nonlinear regression using the same projection matrix as the labeled samples to preserve the intrinsic data structure of unlabeled data. Besides, different from existing methods, we propose an importance differentiation regression strategy to learn hash functions by specially considering the different importance of hash codes learned from the labeled and unlabeled samples. Finally, we develop an efficient discrete optimization method guaranteed with convergence to iteratively solve the hash optimization problem. Experiments on several public multimedia retrieval datasets demonstrate the superiority of our proposed method on both retrieval effectiveness and efficiency. Our source codes and testing datasets can be obtained at https://github.com/ChaoqunZheng/ESMH .

中文翻译：

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具有重要性微分回归的高效半监督多模态散列

多模态哈希通过在模型训练和在线检索阶段协作异构多模态特征来学习紧凑的二进制哈希码，以支持大规模多媒体检索。以前的多模态哈希方法主要集中在有监督和无监督的哈希上。监督散列的性能很大程度上依赖于标记数据的数量，这实际上是昂贵的。无监督散列方法在没有任何监督标签的情况下无法有效地捕获多模态数据的语义相关性。在本文中，我们提出了一个具有重要差分回归 (ESMH-IDR) 模型的高效半监督多模态散列，可以通过从标记和未标记数据中学习来缓解现有问题。具体来说，在本文中，我们开发了一个高效的半监督多模态哈希码学习模块。它以高效的非对称方式学习标记数据的哈希码，同时使用与标记样本相同的投影矩阵执行非线性回归，以保留未标记数据的内在数据结构。此外，与现有方法不同，我们提出了一种重要性微分回归策略，通过特别考虑从标记样本和未标记样本中学习到的哈希码的不同重要性来学习哈希函数。最后，我们开发了一种保证收敛的有效离散优化方法，以迭代地解决哈希优化问题。在几个公共多媒体检索数据集上的实验证明了我们提出的方法在检索有效性和效率方面的优越性。我们的源代码和测试数据集可以在https://github.com/ChaoqunZheng/ESMH .