a Review of Hashing Methods for Multimodal Retrieval

  • Wenming Cao
  • , Wenshuo Feng
  • , Qiubin Lin
  • , Guitao Cao
  • , Zhihai He*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

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.

Original languageEnglish
Article number8963910
Pages (from-to)15377-15391
Number of pages15
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Multimedia
  • deep learning
  • hashing method
  • multimodal retrieval
  • reviews

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