Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Quantized Correlation Hashing for Fast Cross-Modal Search / 3946
Botong Wu, Qiang Yang, Wei-Shi Zheng, Yizhou Wang, Jingdong Wang

Cross-modal hashing is designed to facilitate fast search across domains. In this work, we present a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains. Unlike previous approaches that separate the optimization of the quantizer independent of maximization of domain correlation, our approach simultaneously optimizes both processes. The underlying relation between the domains that describes the same objects is established via maximizing the correlation between the hash codes across the domains. The resulting multi-modal objective function is transformed to a unimodal formalization, which is optimized through an alternative procedure. Experimental results on three real world datasets demonstrate that our approach outperforms the state-of-the-art multi-modal hashing methods.