标题：Dictionary learning based hashing for cross-modal retrieval
作者机构：[Xu, Xin-Shun ] School of Computer Science and Technology, Shandong University, Jinan; 250101, China
会议名称：24th ACM Multimedia Conference, MM 2016
会议日期：15 October 2016 through 19 October 2016
来源：MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
关键词：Cross-modal; Dictionary learning; Hashing; Sparse representation
摘要：Recent years have witnessed the growing popularity of crossmodal hashing for fast multi-modal data retrieval. Most existing cross-modal hashing methods project heterogeneous data directly into a common space with linear projection matrices. However, such scheme will lead to large error as there will probably be some heterogeneous data with semantic similarity hard to be close in latent space when linear projection is used. In this paper, we propose a dictionary learning cross-modal hashing (DLCMH) to perform crossmodal similarity search. Instead of projecting data directly, DLCMH learns dictionaries and generates sparse representation for each instance, which is more suitable to be projected to latent space. Then, it assumes that all modalities of one instance have identical hash codes, and gets final binary codes by minimizing quantization error. Experimental results on two real-world datasets show that DLCMH outperforms or is comparable to several state-of-the-art hashing models. © 2016 ACM.