Survey of Research Progress on Cross-modal Retrieval

Abstract

With the explosive growth of multimedia data on the Internet,single-modal retrieval has been unable to meet the needs of users,and cross-modal retrieval has emerged.Cross-modal retrieval aims to retrieve related data of one modality with data of another modality.Its core task is to extract data features and measure data correlation between different modality.This paper summarizes the recent research progress in the field of cross-modal retrieval,and summarizes the research results in the field of cross-modal retrieval from the perspectives of traditional methods,deep learning methods,manual feature hash coding methods and deep learning hash coding methods.On this basis,the performance of various algorithms in cross-modal retrieval of commonly used standard data sets is compared and analyzed.Finally,the problems of cross-modal retrieval research are analyzed and the future development trend of the field is prospected.

Keywords

• cross-modal retrieval|deep learning|feature extraction|correlation measure