survey

When Location Meets Social Multimedia: A Survey on Vision-Based Recognition and Mining for Geo-Social Multimedia Analytics

Authors:

Xuelong LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 1

Article No.: 1, Pages 1 - 18

https://doi.org/10.1145/2597181

Published: 26 March 2015 Publication History

Abstract

Coming with the popularity of multimedia sharing platforms such as Facebook and Flickr, recent years have witnessed an explosive growth of geographical tags on social multimedia content. This trend enables a wide variety of emerging applications, for example, mobile location search, landmark recognition, scene reconstruction, and touristic recommendation, which range from purely research prototype to commercial systems. In this article, we give a comprehensive survey on these applications, covering recent advances in recognition and mining of geographical-aware social multimedia. We review related work in the past decade regarding to location recognition, scene summarization, tourism suggestion, 3D building modeling, mobile visual search and city navigation. At the end, we further discuss potential challenges, future topics, as well as open issues related to geo-social multimedia computing, recognition, mining, and analytics.

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Index Terms

When Location Meets Social Multimedia: A Survey on Vision-Based Recognition and Mining for Geo-Social Multimedia Analytics
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 1

April 2015

255 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2745393

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Copyright © 2015 ACM.

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Publication History

Published: 26 March 2015

Accepted: 01 December 2013

Revised: 01 October 2013

Received: 01 August 2013

Published in TIST Volume 6, Issue 1

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Deng LLian DHuang ZChen E(2022)Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.313617133:6(2416-2428)Online publication date: Jun-2022
https://doi.org/10.1109/TNNLS.2021.3136171
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https://doi.org/10.1108/JHTT-12-2018-0118
Jiang SJiang CJiang W(2020)Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM toolsISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2020.04.016167(230-251)Online publication date: Sep-2020
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