Weakly-Supervised Online Hashing

Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu

Abstract: With the rapid development of social websites, recent years have witnessed an explosive growth of social images with user-provided tags. Most existing hashing methods for social image retrieval are batch-based which may violate the nature of social images, i.e., social images are usually generated periodically or collected in a stream fashion. Although there exist many online hashing methods, they either adopt unsupervised learning which ignore the relevant tags, or are designed in the supervised manner which needs high-quality labels. In this paper, to overcome the above limitations, we propose a new method named Weakly-supervised Online Hashing (WOH). In order to learn high-quality hash codes, WOH exploits the weak supervision, i.e., tags, by considering the semantics of tags and removing the noise. Besides, we develop a discrete online optimization algorithm, which is efficient and scalable. Extensive experiments conducted on two real-world datasets demonstrate the superiority of WOH.