Multimedia Information Retrieval: From Theory to Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 2046

Special Issue Editors

E-Mail Website
Guest Editor
College of Intelligence and Computing, Tianjin University, Tianjin, China
Interests: multimedia analysis; computer vision

E-Mail Website
Guest Editor
UWA Centre for Medical Research, The University of Western Australia, Perth, Australia
Interests: Computer Vision; Machine Learning

E-Mail Website
Guest Editor
School of Electronic Engineering, Xidian University, Xi'an, China
Interests: Computer Vision; Transfer Learning; Multi-modal Learning

Special Issue Information

Dear Colleagues,

This Special Issue seeks to present and highlight the latest advances on multimedia retrieval and its related broad fields. The main scope of this issue is not only theory of retrieval, indexing, ranking, understanding, etc. of multimedia data and contents, but also practical applications of integration of diverse multimodal data in the context of multimedia retrieval, e.g., social community multimedia data, lifelogging data, user-generated contents, user profile data,  and automatically generated sensor data.

Topics of interest include (but are not limited to):

  • Multimedia content-based search and retrieval;
  • Large-scale and web-scale multimedia retrieval;
  • Multimedia content analysis, understanding, indexing, and ranking;
  • Multimedia data mining and knowledge discovery;
  • Relevance feedback, active learning, and few-shot learning;
  • Semantic descriptors and novel high- or mid-level features;
  • Crowdsourcing and social multimedia;
  • Multimedia retrieval leveraging quality, production cues, style, framing, affect;
  • Multimodal multimedia analysis;
  • User intent and human perception in multimedia retrieval;
  • Mobile multimedia browsing and search;
  • Benchmarks and evaluation methodologies for multimedia analysis/search;
  • Applications of multimedia retrieval, e.g., healthcare, sports, commerce, lifelogs, travel, security, environment.

Dr. Yahong Han
Dr. Yanbin Liu
Dr. Aming Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • multimedia retrieval
  • multimodal analysis
  • content-based multimedia retrieval

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


16 pages, 2341 KiB  
Motion Video Recognition in Speeded-Up Robust Features Tracking
by Jianguang Zhang, Yongxia Li, An Tai, Xianbin Wen and Jianmin Jiang
Electronics 2022, 11(18), 2959; - 18 Sep 2022
Cited by 4 | Viewed by 1352
Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is [...] Read more.
Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets. Full article
(This article belongs to the Special Issue Multimedia Information Retrieval: From Theory to Applications)
Show Figures

Figure 1

Back to TopTop