标题：Supervised robust discrete multimodal hashing for cross-media retrieval
作者：Yan, Ting-Kun ;Xu, Xin-Shun ;Guo, Shanqing ;Huang, Zi ;Wang, Xiao-Lin
作者机构：[Yan, Ting-Kun ;Xu, Xin-Shun ;Guo, Shanqing ;Wang, Xiao-Lin ] School of Computer Science and Technology, Shandong University, Jinan; 250101, China;[Hu 更多
会议名称：25th ACM International Conference on Information and Knowledge Management, CIKM 2016
会议日期：24 October 2016 through 28 October 2016
来源：International Conference on Information and Knowledge Management, Proceedings
关键词：Approximate nearest neighbor search; Cross-media retrieval; Discrete hashing; Learning to Hash; Multimodal hashing
摘要：Recently, multimodal hashing techniques have received considerable attention due to their low storage cost and fast query speed for multimodal data retrieval. Many methods have been proposed; however, there are still some problems that need to be further considered. For example, some of these methods just use a similarity matrix for learning hash functions which will discard some useful information contained in original data; some of them relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error; some of them are not robust to noise. All these problems may degrade the performance of a model. To consider these problems, in this paper, we propose a novel supervised hashing framework for cross-modal retrieval, i.e., Supervised Robust Discrete Multimodal Hashing (SRDMH). Specifically, SRDMH tries to make final binary codes preserve label information as same as that in original data so that it can leverage more label information to supervise the binary codes learning. In addition, it learns hashing functions and binary codes directly instead of relaxing the binary constraints so as to avoid large quantization error problem. Moreover, to make it robust and easy to solve, we further integrate a flexible ℓ2,p loss with nonlinear kernel embedding and an intermediate presentation of each instance. Finally, an alternating algorithm is proposed to solve the optimization problem in SRDMH. Extensive experiments are conducted on three benchmark data sets. The results demonstrate that the proposed method (SRDMH) outperforms or is comparable to several state-of-the-art methods for cross-modal retrieval task. © 2016 ACM.