Scalable Unsupervised Hashing via Exploiting Robust Cross-Modal Consistency
Xingbo Liu, Jiamin Li, Xiushan Nie, Xuening Zhang, Shaohua Wang, Yilong Yin
Abstract
Unsupervised cross-modal hashing has received increasing attention because of its efficiency and scalability for large-scale data retrieval and analysis. However, existing unsupervised cross-modal hashing methods primarily focus on learning shared feature embedding, ignoring robustness and consistency across different modalities. To this end, this study proposes a novel method called scalable unsupervised hashing (SUH) for large-scale cross-modal retrieval. In the proposed method, latent semantic information and common semantic embedding within heterogeneous data are simultaneously exploited using multimodal clustering and collective matrix factorization, respectively. Furthermore, the robust norm is seamlessly integrated into the two processes, making SUH insensitive to outliers. Based on the robust consistency exploited from the latent semantic information and feature embedding, hash codes can be learned discretely to avoid cumulative quantitation loss. The experimental results on five benchmark datasets demonstrate the effectiveness of the proposed method under various scenarios.