Cross-modal hashing (CMH) has recently received increasing attention with the merit of speed and storage in performing large-scale cross-media similarity search. However, most existing cross-media approaches utilize the batch-based mode to update hash functions, without the ability to efficiently handle the online streaming multimedia data. Online hashing can effectively address the preceding issue by using the online learning scheme to incrementally update the hash functions. Nevertheless, the existing online CMH approaches still suffer from several challenges, such as (1) how to efficiently and effectively utilize the supervision information, (2) how to learn more powerful hash functions, and (3) how to solve the binary constraints. To mitigate these limitations, we present a novel online hashing approach named ONION (ONline semantIc autOencoder hashiNg). Specifically, it leverages the semantic autoencoder scheme to establish the correlations between binary codes and labels, delivering the power to obtain more discriminative hash codes. Besides, the proposed ONION directly utilizes the label inner product to build the connection between existing data and newly coming data. Therefore, the optimization is less sensitive to the newly arriving data. Equipping a discrete optimization scheme designed to solve the binary constraints, the quantization errors can be dramatically reduced. Furthermore, the hash functions are learned by the proposed autoencoder strategy, making the hash functions more powerful. Extensive experiments on three large-scale databases demonstrate that the performance of our ONION is superior to several recent competitive online and offline cross-media algorithms.

跨模态哈希（CMH）最近在执行大规模跨媒体相似性搜索时因其速度和存储的优点而受到越来越多的关注。然而，大多数现有的跨媒体方法利用基于批处理的模式更新哈希函数，无法有效处理在线流媒体数据。在线哈希通过在线学习方案增量更新哈希函数，可以有效解决上述问题。尽管如此，现有的在线 CMH 方法仍然面临着几个挑战，例如（1）如何有效地利用监督信息，（2）如何学习更强大的哈希函数，以及（3）如何解决二元约束。为了减轻这些限制，我们提出了一种名为ONION的新型在线哈希方法(在线语义I c aut O encoder hashi NG）。具体来说，它利用语义自动编码器方案来建立二进制代码和标签之间的相关性，从而提供获得更具辨别力的哈希码的能力。此外，所提出的 ONION 直接利用标签内积在现有数据和新数据之间建立联系。因此，优化对新到达的数据不太敏感。配备旨在解决二进制约束的离散优化方案，可以显着减少量化误差。此外，哈希函数是通过所提出的自动编码器策略学习的，使哈希函数更加强大。在三个大型数据库上进行的大量实验表明，我们的 ONION 的性能优于最近几种具有竞争力的在线和离线跨媒体算法。