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


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

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

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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

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Keywords

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

Published Papers (1 paper)

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Research

16 pages, 2341 KiB  
Article
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; https://doi.org/10.3390/electronics11182959 - 18 Sep 2022
Cited by 4 | Viewed by 1352
Abstract
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)
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