A Review of Hashing Methods for Multimodal Retrieval

被引:23
|
作者
Cao, Wenming [1 ,2 ]
Feng, Wenshuo [1 ]
Lin, Qiubin [1 ]
Cao, Guitao [3 ,4 ]
He, Zhihai [2 ]
机构
[1] Shenzhen Univ, Guangdong Multimedia Informat Serv Engn Technol R, Shenzhen 518060, Peoples R China
[2] Univ Missouri, Dept Elect & Comp Engn, Video Proc & Commun Lab, Columbia, MO 65211 USA
[3] East China Normal Univ, Shanghai Key Lab Trustworthy Comp, Shanghai 200062, Peoples R China
[4] East China Normal Univ, MOE Res Ctr Software Hardware Codesign Engn, Shanghai 200062, Peoples R China
来源
IEEE ACCESS | 2020年 / 8卷
基金
中国国家自然科学基金;
关键词
Multimedia; multimodal retrieval; hashing method; deep learning; reviews;
D O I
10.1109/ACCESS.2020.2968154
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the advent of the information age, the amount of multimedia data has exploded. That makes fast and efficient retrieval in multimodal data become an urgent requirement. Among many retrieval methods, the hashing method is widely used in multimodal data retrieval due to its low storage cost, fast and effective characteristics. This review clarifies the definition of multimodal retrieval requirements and some related concepts, then introduces some representative hashing methods, mainly supervised methods that make full use of label information, especially the latest deep hashing methods. The principle and performance of these methods are compared and analyzed. At the same time, some remaining problems and improvement space would be discussed. This review will help researchers better understand the research status and future research directions in this field.
引用
收藏
页码:15377 / 15391
页数:15
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