Gleaning Wisdom from the Past: Towards Label Incremental Learning for Online Hashing with a Plug-and-Play Framework

Benefiting from low storage requirements, high search efficiency, and promising performance, online hashing has attracted considerable attention for large-scale streaming data retrieval. However, most existing online hashing methods implicitly assume that the label space is fixed and fail to effectively handle the label incremental problem. With this motivation, in this article, we introduce a novel supervised online hashing framework named Incremental Plug-and-play Online Hashing (IPOH), which endows prevailing methods with the ability to solve the above issues. Specifically, IPOH could extract the patterns of old classes from the learned hash codes at the last round and then generate the class-wise representations for unseen new labels. With these representations, we could learn the hash codes for streaming data with new labels. Experimental results on two widely used datasets demonstrate the effectiveness of our method in handling class incremental data. We hope this article could convince more researchers to look into this interesting problem, and the source code for the implementation of our IPOH framework has been released at https://github.com/ZCyueternal/IPOH.git .