2017/07/26 by Qing-Yuan Jiang, Jiang, Qing-Yuan, Wu-Jun Li +1 · 6 citations
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Robotics and Sensor-Based Localization #Video Surveillance and Tracking Methods #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1707.08325
arxiv created 2017/07/26 · openalex publication_date 2017/07/26 · arxiv updated 2017/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications.