Special Issue on “Multimedia Analysis with Collective Intelligence”

Multimedia analysis has attracted extensive research interests and nowadays it forms the basis of a wide variety of applications and services, such as search, recommendation, advertising, and personalization. Existing technical approaches usually need to be guided with prior knowledge, such as data with labels. But collecting such knowledge is usually not easy, and the problem becomes even worse when we need to deal with big data. Therefore, a lot of research efforts turn to mine the knowledge by exploring collective intelligence. For example, crowds of grassroots users were allowed to generate, annotate and share their own data on public websites like Facebook, Flickr, and Youtube. Collective intelligence is widely embedded in these data as well as their tags, comments and ratings. Such intelligence can be leveraged in multimedia classification, search, recommendation, etc. Moreover, user behaviors interacting with computer and web also contain collective intelligence implicitly, such as users’ click-through, browsing, and viewing history. The widely existed collective intelligence offers us opportunities to tackle the difficulties in multimedia analysis. This special issue is intended to bring together the greatest research efforts along this direction and introduce them to readers.

Scope

The scope of this special issue is to cover all aspects that relate to multimedia analysis with collective intelligence. Topics of interest include, but are not limited to

  • Automatic multimedia data collection and labeling.
  • Interactive multimedia data collection and labeling.
  • Label denoising and refinement.
  • Multimedia feature learning with collective intelligence, including global and local feature extraction, keypoint detection, visual vocabulary construction, feature selection, etc.
  • Multimedia modeling with collective intelligence and applications, including classification, clustering, recommendation, etc.
  • User behavior modeling and mining.
  • Social media pattern recognition and mining.
  • Novel crowdsourcing systems, techniques, and interfaces.
  • Information for Authors

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the ‘Journal of Visual Communication and Image Representation’ at http://ees.elsevier.com/jvci/. When submitting via this page, please select “VSI:CollectiveIntelligence” as the Article Type. Prospective authors should submit high quality, original manuscripts that have not appeared, nor are under consideration, in any other journals. All submissions will be peer reviewed following the JVCI reviewing procedures.

Important Dates

  • Manuscript Submission Deadline: February 28, 2017
  • Notification of Acceptance/Rejection: August 15, 2017
  • Final Manuscript Due to JVCI: August 31, 2017
  • Expected Publication Date: December, 2017

See also the call for submissions.