Human activity analysis is an important area of computer vision and pattern recognition research with the rapid development of computing and sensing technologies such as the emergence of social network and wearable devices. It is becoming more and more critical to develop human-centered multimedia analytics including human behavior recognition, personal data mining, experiences of wearable-device applications, intelligent surveillance, semantic relationship analysis between heterogeneous multimedia. The development of the Internet of Things opens up great potential.
Many data mining or machine learning techniques have recently been successfully developed and applied to human activity analysis. For example, smart devices (e.g. Microsoft’s SenseCam, Google Glass, smart phone, smart watches, portable EEG acquisition devices, etc.) facilitate the capture and collection of human activity multimedia; sparse representation is widely employed for human activity recognition; multi-view learning algorithms significantly boost the performance of human re-identify; manifold learning algorithms dramatically enhance the recognition rates in human behavior analysis where there is only a few labeled samples; and deep learning has produced promising results in many human-centered applications.
Motivated by the inclination to collect a set of recent advances and results in these related topics, provide a platform for researchers to exchange their innovative ideas and attractive improvements on human activity analysis, and introduce interesting utilizations of data mining and machine learning algorithms for particular human-centered applications, this workshop will target emergent data mining methods for human activity analysis.
To summarize, this workshop welcomes a broad range of submissions developing and using data mining techniques for human activity analysis. We are especially interested in 1) theoretical advances as well as algorithm developments in data mining for human activity analysis, 2) reports of practical applications and system innovations in human activity analysis, and 3) novel data sets as test bed for new developments, preferably with implemented standard benchmarks. The following list suggests topics of interest (but not limited to):
• Multi-view Learning algorithms for Human Activity Analysis
• Sparse and/or Manifold Learning for Human Activity Representation
• Human Action Recognition in Images/Videos
• Human Behavior Analysis
• Human-centered Social Media Analytics
• Smart Computing for Personal Data Mining
• Deep Learning for Human-centered Media
• Parallel Computing for Human-centered Media
• Experiences of wearable-device applications
• Human-centered Data Visualization
• Analytics developed from Computational Psychoanalysis and related underpinnings.
Accepted papers will be included in the IEEE ICDM 2016 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS).
August 12, 2016: Due date for full workshop papers
September 13, 2016: Notification of workshop papers acceptance to authors
September 20, 2016: Camera-ready deadline for accepted papers
December 12, 2016: Workshop date
The page limit of workshop papers is 8 pages in the standard IEEE 2-column format (http://www.ieee.org/conferences_events/conferences/publishing/templates.html), including the bibliography and any possible appendices.
All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2016 submission guidelines available at http://icdm2016.eurecat.org/.
Papers should be submitted in PDF format, electronically, using the CyberChair submission system (https://wi-lab.com/cyberchair/2016/icdm16/scripts/submit.php?subarea=S08&undisplay_detail=1&wh=/cyberchair/2016/icdm16/scripts/ws_submit.php).
Every workshop paper must have at least one paid registration in order to be published, no matter whether the author is a workshop organizer or an invited speaker.
Dr. Xinmei Tian
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, China, 230027.
Phone: +86 551-63600281
Dr. Weifeng Liu,
China University of Petroleum (East China),
#66, Changjiang Road West, Huangdao District, Qingdao 266480, China,
Prof. Fionn Murtagh
Department of Computing and Mathematics, University of Derby, Derby DE22 1GB, UK, and Department of Computing, Goldsmiths, University of London, London, SE14 6NW, UK..