Open AccessResearch and Implementation of Multi-modal Video Retrieval System Based on Deep Learning
Author(s) -
Bing Qi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012026
Subject(s) - computer science , video retrieval , information retrieval , modal , workload , deep learning , image retrieval , multimedia , the internet , data retrieval , artificial intelligence , human–computer information retrieval , visual word , world wide web , image (mathematics) , search engine , chemistry , polymer chemistry , operating system
At present, keyword-based video retrieval is gradually difficult to adapt to the needs of the rapid development of the Internet due to its strong subjectivity and huge workload. As a result, multi-modal video retrieval based on deep learning has appeared. This retrieval method can conduct video retrieval through multiple methods such as text, image, and video, which fully meets the different retrieval needs of different users, and significantly improves the accuracy and effectiveness of video retrieval. Based on this, this article discusses in detail the design of a multi-modal video retrieval system based on deep learning, analyzes and designs each functional module of the system to provide reference for future related work.