Efficient Supervised Discrete Multi-View Hashing for Large-Scale Multimedia Search

• Published in: IEEE transactions on multimedia Vol. 22; no. 8; pp. 2048 - 2060
• Main Authors: Lu, Xu, Zhu, Lei, Li, Jingjing, Zhang, Huaxiang, Shen, Heng Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Binary codes; Hashing; Kernel; Learning; Mapping; multi-view; Multimedia; multimedia search; Optimization; Quantization (signal); Redundancy; Retrieval; Rounding; Search problems; Searching; Semantics; Training
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2019.2947358

Hashing has recently received substantial attention in large-scale multimedia search for its extremely low-cost storage cost and high retrieval efficiency. However, most existing hashing techniques focus on learning hash codes for single-view or cross-view retrieval. It is still an unsolved problem that how to efficiently learn discriminative binary codes for multi-view data that is common in real world multimedia search. In this paper, we propose an efficient Supervised Discrete Multi-view Hashing (SDMH) to solve the problem. SDMH first properly detects the shared binary hash codes, with an integrated multi-view feature mapping and latent hash coding, by exploiting the complementarity of different view-specific features and removing the involved inter-view redundancy. To further enhance the discriminative capability of hash codes, SDMH directly represses the explicit semantic labels of data samples with their corresponding binary codes. Different from most existing multi-view hashing methods that adopt "relaxing+rounding" hash optimization strategy or the discrete optimization method based on discrete cyclic coordinate descent, an efficient augmented Lagrangian multiplier (ALM) based discrete hash optimization method is developed in this paper to optimize the hash codes within a single step. Experimental results on four benchmark datasets demonstrate the superior performance of the proposed approach over state-of-the-art hashing techniques, in terms of both learning efficiency and retrieval accuracy.