Guest Editorial: Knowledge-Based Multimedia Computing

Guest Editorial: Knowledge-Based Multimedia Computing Multimed Tools Appl (2017) 76:24955–24959 DOI 10.1007/s11042-017-5212-x GUEST EDITORIAL 1 2 3 Liang Li & Zi Huang & Zheng-Jun Zha & Shuqiang Jiang Published online: 2 October 2017 Springer Science+Business Media, LLC 2017 The rapid advances in multimedia technology have resulted in a proliferation of multimedia data on the Internet. This is reflected in the success of the social networks, such as Facebook, Twitter, YouTube, Flickr, and Pinterest, which dramatically increased the volume of community-shared media, including images and videos. Although these websites allow users to annotate and rate them, the accurate annotations of online media are very rare and unsatisfactory. Thus, accurately understanding this multimedia content is a very significant and challenging issue. The scenario of online multimedia understanding has usually large number of categories with unconstrained domains and noise. Recent progress on visual genome dataset and deep model open an exciting new era of knowledge-based multimedia computing, which can provide a knowledge base of images and capture the complex content with domain-specific knowledge. Moreover, some works about dense image captioning, visual relationship detec- tion, visual question answering, knowledge inference, and social network knowledge graph also provide insight into tackling the knowledge-based multimedia computing. The prior * Liang http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Guest Editorial: Knowledge-Based Multimedia Computing

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-5212-x
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl (2017) 76:24955–24959 DOI 10.1007/s11042-017-5212-x GUEST EDITORIAL 1 2 3 Liang Li & Zi Huang & Zheng-Jun Zha & Shuqiang Jiang Published online: 2 October 2017 Springer Science+Business Media, LLC 2017 The rapid advances in multimedia technology have resulted in a proliferation of multimedia data on the Internet. This is reflected in the success of the social networks, such as Facebook, Twitter, YouTube, Flickr, and Pinterest, which dramatically increased the volume of community-shared media, including images and videos. Although these websites allow users to annotate and rate them, the accurate annotations of online media are very rare and unsatisfactory. Thus, accurately understanding this multimedia content is a very significant and challenging issue. The scenario of online multimedia understanding has usually large number of categories with unconstrained domains and noise. Recent progress on visual genome dataset and deep model open an exciting new era of knowledge-based multimedia computing, which can provide a knowledge base of images and capture the complex content with domain-specific knowledge. Moreover, some works about dense image captioning, visual relationship detec- tion, visual question answering, knowledge inference, and social network knowledge graph also provide insight into tackling the knowledge-based multimedia computing. The prior * Liang

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Oct 2, 2017

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