Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this work proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space. Then, a structured hash model is derived and optimized in a passive-aggressive way. Theoretical analysis on the upper bound of the cumulative loss for the proposed online hash model is provided. Furthermore, we extend our online hashing from a single-model to a multi-model online hashing that trains multiple models so as to retain diverse online hashing models in order to avoid biased update. The competitive efficiency and effectiveness of the proposed online hash models are verified through extensive experiments on several large-scale datasets as compared to related hashing methods.

提出了一种在线哈希模型来适应流数据的在线学习，并通过衡量两个数据样本之间在汉明空间中的相似损失来导出结构化哈希模型以被被被优化，该工作对所提出的在线哈希模型累积损失的上界进行了理论分析，并将在线哈希从单一模型扩展到多模型在线哈希，通过在多个大规模数据集上的大量实验验证了所提出的在线哈希模型的竞争效率和效果而言，相比相关的哈希方法。